SkyShot - Volume 1, Issue 1: Autumn 2020

The inaugural issue of SkyShot, an online publication for promoting understanding and appreciation for outer space. As an international community, we share the work of undergraduate and high school students through a multidisciplinary, multimedia approach. Features research papers, astrophotography, informative articles, guides, and poetry in astronomy, astrophysics, and aerospace.

The inaugural issue of SkyShot, an online publication for promoting understanding and appreciation for outer space. As an international community, we share the work of undergraduate and high school students through a multidisciplinary, multimedia approach. Features research papers, astrophotography, informative articles, guides, and poetry in astronomy, astrophysics, and aerospace.


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Autumn 2020

Volume I, Issue I



Astronomy Made Accessible

The History and Potentially

Imminent Explosion of

Betelgeuse p. 10

Research: Analysis of

Supernova SN 2019hyk p. 12

Computational Astrophysics

Advancements of 2020 p. 39

Advancements in Aerospace p. 44



Recap and


p. 8



on the Cheap: A


p. 20



of gauss”

“starry dreams”

p. 51


SkyShot Autumn 2020

Letter from the Editing Team

On behalf of the editing team, welcome to the first publication of SkyShot! Our

goal with this magazine is to launch a platform where students can share their spacerelated

works with a wide audience. In this inaugural issue, we are excited to spotlight

some great work that our contributors have created, with a wide range of topics

covering research, articles, poetry, astrophotography, and more. We sincerely thank all

contributors for sharing their passion and hard work with us. We hope you enjoy this

season’s publication, and we look forward to growing this platform with all of you!

Happy reading!

The SkyShot Team

Editing Team

Founder: Priti Rangnekar

President: Rutvik Marathe

Executive Directors:

Naunet Leonhardes-Barboza

Victoria Lu

Carter Moyer

Vighnesh Nagpal

Ezgi Zeren

Head of Astrophysics: Andrew Tran

Head of Content Management: Anavi Uppal

Head of Peer Review: Alexandra Masegian

Peer Reviewer: Feli X

Front and back cover


Rosette Nebula (NGC


© Jonah Rolfness


SkyShot Autumn 2020

Founder’s Note

Against all odds amidst a pandemic, 2020 has been a remarkable year for

astronomy, astrophysics, and aerospace alike. As the first crewed launch from U.S.

soil in nearly a decade, the Crew Dragon Demo-2 ushered in high spirits. Around

the world, astronomers embarked on a quest to capture the magnificence of Comet

NEOWISE. During the autumn, three phenomenal scientists earned the Nobel

Prize in Physics for their research in black holes and cosmology. Time and time

again, the space sciences prove their unique worth as a force for uniting humanity.

Simultaneously, I was inspired by the sheer diversity of space-related endeavors

pursued by our young generation. As my exoplanet research group pored

over online transit data, the essence of teamwork transcended boundaries of

time zones or location. I saw sparks of joy whenever fellow Summer Science

Program alumni discussed advice about imaging nebulas or shared their spaceinspired

arts. It was clear that students deserved a platform that showcased their

efforts in space science while serving as a welcoming, nurturing community.

An initiative consisting of five projects, “Science Connect” was founded with

the mission of amplifying multidisciplinary education through opportunities in

hands-on problem-solving and communication. SkyShot became our flagship

project as a stunning paragon of collaboration and fusion of disciplines.

After all, the universe is not based upon arbitrary divisions in academic fields - it

simply exists, and we strive towards appreciation and understanding. Outer space

presents an unparalleled juxtaposition of “science” and “art.” On one hand, the

night skies inspire humility and awe at the vast expanse above. On the other, we

are compelled to ask fundamental questions regarding astrophysical processes

and embrace the final frontier through engineering advances. Above all, our innate

curiosity and connection with outer space drive humankind to reach its full potential.

As you read SkyShot, immerse yourself in the multifaceted nature of the cosmos.

Engaging with the wonders of outer space is for everyone, regardless of demographics,

background, or academic interest. I hope you can find and reflect upon your unique

avenues of doing so. Together, we will launch into a new era of unbridled intellectual

growth and exploration.

- Priti Rangnekar


SkyShot Autumn 2020

p. 22

p. 28

p. 46

p. 48


SkyShot Autumn 2020

6 Astronomical Sightseeing in 2020

Abby Kinney

8 Comet NEOWISE and NEOWISE Astrophotography

Victoria Lu, Anavi Uppal, and Owen Mitchell

10 The History and Potentially Imminent Explosion of Betelgeuse

Alexandra Masegian

12 Photometric and Spectroscopic Analysis of the Type II-P Supernova SN 2019hyk

Sofia Fausone, Timothy Francis Hein, Anavi Uppal, and Zihang Wang

16 On Methods of Discovering Exoplanets

Vighnesh Nagpal

20 Astrophotography on the Cheap: A Guide

Cameron Woo

22 Astrophotography Gallery

Ryan Caputo, Owen Mitchell, Jonah Rolfness, Nathan Sunbury, Anavi Uppal, Cameron Woo, Wilson Zheng

28 Removing Noise and Improving the Quality of Astronomical Observations with Fourier Transforms

Ezgi Zeren

39 Computational Astrophysics Advancements of 2020

Priti Rangnekar

44 Advancements in Aerospace

Rutvik Marathe

46 Access to Space

Carter Moyer

In This Issue

48 Understanding the Chronology of the Universe

Andrew Tran

51 2002 KM6 (99795)

Naunet Leonhardes-Barboza

51 method of gauss

Naunet Leonhardes-Barboza

52 tessellated constellations

Alex Dong

53 starry dreams

Alex Dong

54 images of the past

Naunet Leonhardes-Barboza

55 unseen skies

Alex Dong

56 Astrophotography Index

59 Educational Opportunities

60Contributor Biographies


SkyShot Autumn 2020


Sightseeing in


Abby Kinney

Mars at its Best

This autumn has been a great time to view Mars. On October

13th, the planet reached opposition, meaning it appeared opposite

the Sun in the sky, reaching its highest point in the sky at


However, Mars was a spectacular sight earlier this season as

well. Mars dramatically increased brightness, eventually surpassing

Jupiter’s brightness on September 24th, and reached a magnitude

of -2.6 at opposition. Additionally, the apparent size of

Mars’ disk was approximately 22 arcseconds [2].

Perhaps you’re reading this in disappointment remembering

the last Mars opposition in 2018 when Mars reached a magnitude

of -2.8 and disk size of about 24 arcseconds [1].

But for Northern Hemisphere observers this 2020 opposition

had something else going for it: Mars’s altitude in the sky was

much higher. In 2018, Mars appeared in the constellation of Capricornus.

But this year, Mars appeared in Pisces, which is further

North in the sky [2].

Whether you were viewing the red planet with your eyes, binoculars,

or telescopes, it fulfilled its promise of being a breathtaking

fall sight.

Source: EarthSky Community Photos.


[1] Dickinson, David. “Enter the Red Planet: Our Guide to

Mars Opposition 2018.” Universe Today, 18 July 2018, www.universetoday.com/139420/enter-the-red-planet-our-guide-to-marsopposition-2018/.

[2] Lawrence, Pete. “Mars Opposition 2020: Get Ready to Observe

the Red Planet.” Skyatnightmagazine, 24 Aug. 2020, www.


[3] Pasachoff, Jay M. Stars and Planets. Houghton Mifflin, 2000.

Halloween’s Blue Moon

Everyone’s heard the phrase “once in a blue moon,” but

this Halloween, we had a chance to see such a moon. However,

the term “blue moon” is actually quite misleading.

First, blue moons are not necessarily blue. Rather than indicating

a color, a “blue” moon usually refers to the second

full moon of a calendar month [1].

But this now-common definition was actually a mistake

created by the misinterpretation of the Maine Farmer’s Almanac

in a 1946 Sky and Telescope article titled “Once in a

Blue Moon.” The mistake was then popularized by a reading

of this article on the radio program StarDate. Eventually,

this new definition even found its way into the popular

game Trivial Pursuit [2].

The Maine Farmer’s Almanac defined a “blue moon” as

the third full moon of a season with four full moons. This

may seem like a distinction without a difference, but if we

look at this Halloween blue moon, it was actually the second

full moon of Autumn. Thus, by the almanac’s definition,

it would not be a full moon. In fact, the next seasonal

blue moon is in August of 2021, despite that month having

only one full moon.

In addition to lacking their eponymous blue hue, blue

moons are not as uncommon as the ubiquitous phrase

would suggest. Because most calendar months are longer

than the moon’s approximately 29.5 day synodic period

(the period of its phases), a blue moon can occur if the first

full moon was sufficiently early in the month. Thus, a blue

moon typically happens every two to three years and will always

occur on either the 30th or 31st [1]. While blue moons

in general are fairly common, a blue moon on Halloween is

more special: the last blue moon to fall on October 31st was

in 1974, and it won’t happen again until 2039 [4].

In light of the surprising frequency of blue moons, you

may be wondering how the common refrain “once in a blue

moon” came to indicate something rarely occurring; however,

the refrain actually predates our modern usage. It was

used to indicate something that was as absurd as the Moon

turning blue. Over time, the phrase came to mean something

similar to never or very rarely [2].

But the moon can actually appear bluish. While the light

from the moon is reflected from the Sun and, therefore,

virtually identical to daylight, atmospheric conditions can

change the appearance of the moon [3]. For instance, after

the eruption of the volcano Krakatoa in 1883, volcanic ash

in the air was the perfect size to scatter the redder colors of

the moonlight, giving the moon a bluer tint [1].

So whether you were handing out candy or trick-ortreating

yourself this year, that not-so-blue blue moon was

definitely worth looking at.


SkyShot Autumn 2020


[3] Sky Events Calendar by Fred Espenak and Sumit Dutta

The Great Conjunction

of 2020

The full moon on October 31, 2020, Hungary. Source: USA



[1] Dunbar, Brian. “Blue Moon.” NASA, NASA, 7 July 2004,


[2] Hiscock, Philip. “Blue Moons - Origins and History of the

Phrase.” Sky & Telescope, 20 Apr. 2020, skyandtelescope.org/


[3] O’Meara, Stephen James. “The Blue Moons.” Astronomy.com,

12 Sept. 2016, astronomy.com/magazine/stephen-omeara/2016/09/the-blue-moons.

[4] Sky Events Calendar by Fred Espenak and Sumit Dutta


A Moonless Meteor


Every year, around mid-December, stargazers are treated

to the Geminid Meteor Shower. This meteor shower is

thought to be caused by the Earth crossing a swath of small

particles leftover from comet 3200 Phaethon. The particles

entering the atmosphere comprise the wonderful meteor

shower that we witness each year [2].

This year, however, is a special experience, because the

maximum of the 2020 Geminid Meteor Shower falls on December

14th, which nearly coincides with the new moon.

This means that there will be no moon in the night sky to

interfere with seeing the fainter meteors. During the maximum,

60 to 120 meteors may be seen each hour. As a general

rule, the higher the two bright stars, Castor and Pollux of

Gemini, are in the sky, the more meteors will be seen. This

is because these stars in Gemini are near the radiant of the

Geminid Meteor Shower, which is the point in the sky that

appears to be the origin of the meteors [2].

This year’s moonless Geminid Meteor Shower has the potential

to be a spectacular way to spend a December night. So

get outside and look up!


[1] Pasachoff, Jay M. Stars and Planets. Houghton Mifflin,


[2] McClure, Bruce, and Deborah Byrd. “This Weekend’s

Geminid Meteor Shower.” EarthSky, 2019, earthsky.org/

All summer long, the two largest planets of our solar system,

Jupiter and Saturn, have shared the evening sky. But

on December 21st, the two planets will reach conjunction,

meaning they will share the same right ascension. While

the actual time of conjunction will occur at 13:22 UTC, the

planets will appear very close all night and can be observed

getting closer over the course of several days leading up to

their conjunction. But at their closest, they will be separated

by only 6 arcminutes, roughly a fifth the apparent diameter

of a full moon.

Conjunctions of Jupiter and Saturn are not rare, per se:

they occur approximately every 20 years due to the difference

in the orbital periods of Saturn and Jupiter. Saturn,

being farther from the Sun, takes about 30 years to orbit,

while Jupiter takes about 12 years. This means that Jupiter

travels about 30 degrees in the sky every year, while Saturn

travels only 12 degrees [2]. This difference in rates means

every year Jupiter gains about 18 degrees on Saturn. Consequently,

it takes approximately 20 years for Jupiter to “lap”

Saturn. Thus, there are approximately 20 years in between

conjunctions of Saturn and Jupiter. However, this year’s

conjunction is truly special: Jupiter and Saturn have not appeared

so close in the sky since 1623 and will not again until


With the naked eye, Jupiter and Saturn may appear to be

one point of light, but with just a small pair of binoculars

they can be resolved into two small disks with Jupiter being

the brighter of the two. With a telescope, many more

features can be seen, including cloud bands on Jupiter, the

rings of Saturn, Jupiter’s Galilean moons (Io, Europa, Callisto,

and Ganymede), and Saturn’s largest moon, Titan [1].

The location of Jupiter and Saturn low in the southwestern

sky may make viewing difficult, especially if there are

trees or other obstructions in that region. Additionally, the

planets will only be visible for a short time after sunset [1].

In spite of these challenges, this year’s great conjunction is

an historic one and the meeting of these two majestic planets

will be a sight worth seeking out.


[1] Money, Paul. “Upcoming Conjunctions in the Night

Sky, and How to See Them.” Skyatnightmagazine, 26 Aug.

2020, www.skyatnightmagazine.com/advice/skills/conjunctions-in-night-sky-how-see/.

[2] Etz, Donald V. “Conjunction of Jupiter and Saturn.”

Journal of the Royal Astronomical Society of Canada, vol.

94, Aug. 2000, pp. 174–178.

[3] Pasachoff, Jay M. Stars and Planets. Houghton Mifflin,



SkyShot Autumn 2020


Victoria Lu

July of 2020 was a notable month for stargazers as Comet

NEOWISE appeared in night skies. The comet was the brightest

to appear in the Northern Hemisphere in decades and was

visible to the naked eye.

Comet NEOWISE was discovered on March 27, 2020 by

astronomers using NASA’s Wide-field Infrared Survey Explorer

(WISE) telescope. WISE launched in 2009 and utilizes infrared

wavelengths to search for galaxies, cold stars, and near-earth

objects such as asteroids and comets. The telescope’s infrared

channels detected the heat signatures of the comet. From its

signature, scientists determined that the comet was about 5 kilometers

across and covered with sooty particles leftover from

the birth of the solar system [2].

Comets are balls of frozen gas, dust, and rock that orbit the

sun [4]. When a comet such as NEOWISE nears the sun, the

increased heat forms a coma around the nucleus. The coma—

which can be considered an atmosphere composed of particles

and gases—is blown to form a tail. Comet NEOWISE has

been observed to have both an ion tail and a dust tail. A dust

tail forms when dust within the nucleus is forced out by solar

radiation pressure. An ion tail, on the other hand, is birthed

when ultraviolet radiation forces electrons to eject from the

coma. The particles ionize and form a plasma, which interacts

with high solar winds to form a tail. Such tails can stretch for

millions of miles [3].

NEOWISE was not considered a “great comet” (an exceptionally

brilliant comet with a long tail), but it dazzled viewers

nonetheless. Its brightness enabled viewers to observe it with

the naked eye in dark locations, or with the assistance of binoculars

and telescopes [1]. Comet NEOWISE’s proximity to the

Big Dipper made it easier for inexperienced viewers to spot.

NEOWISE was difficult to see in areas with high light pollution

but was still visible after some persistence.

On July 22, the comet reached perigee, passing as close to

the Earth as it would come at a distance of 103.5 million km.

Comet NEOWISE had been fading since it reached perihelion

(the closest point to the sun) on July 3rd, but its approach to

Earth made the dimming less noticeable. After reaching perigee,

however, the comet steadily dimmed as it grew further

from Earth. On July 4th the comet had a magnitude of +1.6, but

in subsequent weeks it dimmed, becoming an 8th magnitude

object towards the end of mid-August. Magnitude is a measure

of brightness, with higher numbers signifying dimmer objects


Once gone, this comet will not appear for another 6,800

years. One fact is for certain—Comet NEOWISE put on a dazzling

display for viewers worldwide before bidding goodbye.


[1] Furfaro, E. (2020, July 14). How to See Comet

NEOWISE. Retrieved August 25, 2020, from https://


[2] Hartono, N. (2020, July 08). Comet NEOWISE

Sizzles as It Slides by the Sun. Retrieved August 25,

2020, from https://www.nasa.gov/feature/jpl/cometneowise-sizzles-as-it-slides-by-the-sun-providing-atreat-for-observers

[3] Isaacs-Thomas, I. (2020, July 28). How to spot

Comet NEOWISE before it disappears for thousands

of years. Retrieved August 25, 2020, from https://


[4] NASA. (n.d.). Comets. Retrieved August 25,

2020, from https://solarsystem.nasa.gov/asteroids-comets-and-meteors/comets/overview/?page=0

[5] Rao, J. (2020, July 24). The curtain is about to

come down on Comet NEOWISE. Retrieved August

25, 2020, from https://www.space.com/comet-neowise-is-dimming.html

Comet NEOWISE, as observed in mid-July

from Bozeman, Montana.

© Owen Mitchell


SkyShot Autumn 2020

Comet NEOWISE (Zoomed), as observed on July 19 in Orlando, Florida.

© Anavi Uppal

Comet NEOWISE (wide), as observed on July 19 in Orlando, Florida.

© Anavi Uppal


SkyShot Autumn 2020

The History and Potentially

Imminent Explosion of


Alexandra Masegian

The changing surface of fading Betelgeuse. Source: NASA, 2020.

Sitting high in the left shoulder of the Orion constellation,

the massive star known as Betelgeuse is

nearing the end of its life. As one of the largest and

most luminous stars in the sky, Betelgeuse has been

identified as an M1-2 type red supergiant star [1].

When it dies, it will undergo a catastrophic process

known as a supernova, flinging its outer layers into

space in a massive explosion and leaving behind a

core so dense that it will likely become a black hole.

The only question is when that supernova will occur

— a question that, over the course of the past

year, astrophysicists thought was on the verge of

being answered.

Betelgeuse is known to be a semi-regular variable

star, meaning that its brightness fluctuates periodically

on multiple timescales. Because it is so bright,

these fluctuations are often visible to the naked eye

and were noticed even by the earliest of astronomers.

In fact, the first recorded instance of Betelgeuse’s

variability dates back to 1836. The event was

described in Sir John Herschel’s 1849 Outlines of

Astronomy, who wrote, “The variations of Alpha

Orionis, which were most striking and unequivocal

in the years 1836-1840, within the years since

elapsed became much less conspicuous” [2]. By

piecing together observations like Herschel’s and

more modern data, astrophysicists have deduced

that Betelgeuse primarily pulsates on a timescale of

almost 425 days, with secondary periods of 100-180

days and 2,153 days (5.9 years) [3]. This means that

slight fluctuations in the star’s brightness are both

commonplace and expected — within the confines

of the expected pattern, of course.

Over the course of the past year, however, the

massive star’s brightness has been fluctuating in

ways that defy this usual pattern. In October of

2019, Betelgeuse began to dim at a point in its cycle

where it normally would have been bright. Though

the change was not noticeable at first, by December

of 2019 the supergiant had lost over two-thirds

of its usual brilliance, enough to reduce it from

one of the top ten brightest stars in the sky to the

twenty-first. [3] The star’s dimming was the most

severe since precise monitoring of its atmosphere

began nearly a century ago, and it lasted much longer

than would be normal for the star’s typical cycle.

(If the dimming was just a product of two of the

star’s cycles overlapping at their minimum points,

for instance, it would have only lasted a few weeks

rather than several months.) Astronomers around


SkyShot Autumn 2020

the world began to take notice. Was Betelgeuse

on the verge of supernova?

Little is known about how massive

stars behave in the years leading up to

their explosive deaths. Though historical

evidence indicates that humanity has

witnessed supernovae before, the last

such event to occur in our galaxy was in

1604, long before the advent of modern

telescopes and observing technology. [4]

Even though we have never been able to

observe a star during its final moments

before the supernova, astrophysicists

have made predictions as to what some

of the early warning signs could be. One

of those possible signs is what astrophysicist

Sarafina Nance calls “insane and violent

mass loss.” [5] In theory, a dying

star will shoot a large portion of its mass

into space right before its death, which

could cause the star to appear dimmer

as clouds of ejected dust block its light

from reaching the Earth. Betelgeuse’s

sudden and significant dimming, therefore,

seemed to be a sign that the red supergiant

was in the throes of death.

As astronomers scrambled to develop

theories for what could be causing the

star’s abrupt change, Harvard-Smithsonian

astrophysicist Andrea Dupree and

her team turned to the Hubble Space

Telescope, which they started using to

monitor Betelgeuse in early January of

2019. They were able to isolate several

months of ultraviolet-light spectroscopic

observations of the star in the time

leading up to its dimming, and analysis

of the data revealed signs of dense, hot

material moving through the star’s atmosphere

in the months of September,

October, and November 2019. [6] Traveling

at nearly 200,000 miles per hour, the

material continued beyond Betelgeuse’s

visible surface and out into space at

around the same time that the star underwent

its most significant dimming.

Dupree theorizes that once the material

had separated from the incredibly hot

stellar surface, it was able to cool enough

to form a large gas cloud, which would

have blocked a large portion of Betelgeuse’s

light and made it appear much

dimmer to us here on Earth. [6] Meanwhile,

the star’s normal pulsational cycle

continued as usual, the behavior of

the star’s outer atmosphere returning to

normal even as the dust lingered.

It is not yet understood what caused

the stellar outburst, though Dupree and

her colleague Klaus Strassmeier of the

Leibniz Institute for Astrophysics in

Potsdam think that it may have been a

result of the star expanding in its pulsation

cycle at the same time that a pocket

of material underneath the star’s surface

was experiencing an upwelling. The

surge of force accompanying Betelgeuse’s

expansion could have propelled the hot

plasma in the convection cell outward,

providing it with enough momentum

to escape the star’s atmosphere. Though

the star has since stabilized and seems to

have returned to its normal pulsational

cycle and brightness, the mass ejection

that Dupree observed has been found to

contain nearly twice the amount of material

that is typical of a Betelgeuse outburst.

[6] The question of whether or not

it is on the brink of supernova remains

an open one.

When Betelgeuse eventually does

reach the end of its life, the resulting explosion

will be bright enough to be visible

during the day and cast shadows at

night here on Earth. Though our planet

is far enough away from the massive

star to avoid the majority of the radiation

that the supernova will produce, the

afterimage of the supergiant’s death will

linger in the sky for months, serving as a

stark reminder of the vastness and beauty

of our universe. The star’s odd behavior

this past year might not be the signal

we’re looking for to indicate Betelgeuse’s

imminent death, but it certainly is a sign

that the star is growing more unstable

— and, whenever it does finally explode,

its death will undoubtedly be one of the

most exciting astrophysical events of the



[1] Keenan, Philip C.; McNeil, Raymond

C. (1989). “The Perkins catalog of

revised MK types for the cooler stars”.

Astrophysical Journal Supplement Series.

71:245. DOI: 10.1086/191373.

[2] Davis, Kate. Alpha Orionis (Betelgeuse).

American Association of Variable

Star Observers. <https://www.aavso.


[3] King, Bob. (2019 December 21). Betelgeuse

is Dimming… Why? Sky & Telescope.


[4] Vink J. (2016) “Supernova 1604,

Kepler’s Supernova, and Its Remnant.”

In: Alsabti A., Murdin P. (eds) Handbook

of Supernovae. Springer, Cham.


[5] Drake, Nadia. (2019 December 26).

A giant star is acting strange, and astronomers

are buzzing. National Geographic.



[6] Dupree, Andrea et al. (2020). “Spatially

Resolved Ultraviolet Spectroscopy

of the Great Dimming of Betelgeuse.”

The Astrophysical Journal. 899:1. DOI:



SkyShot Autumn 2020

Photometric and Spectroscopic

Analysis of the Type II-P Supernova

SN 2019hyk

Sofia Fausone

Healdsburg High School

Timothy Francis Hein

Los Altos High School

Anavi Uppal

Windermere Preparatory School

Zihang Wang

Webb School of California

Dated: August 2, 2019


Over a period of four weeks, we performed spectroscopy and BVRI color photometry on SN 2019hyk, a

supernova located in galaxy IC 4397. Using the telescopes at Yale University’s Leitner Family Observatory

and on iTelescope.net, we took images throughout July 2019 to generate a light curve of the supernova.

After evaluating the shape of the light curve and the emission spectrum, we conclude that SN 2019hyk is a

type II-P supernova.


The discovery, observation, and analysis of supernovae

provide us with valuable insight into the processes

and physics behind these events. Supernovae are largely

responsible for distributing heavy metals throughout

space, and are used as standard candles for measuring

cosmological distances. Investigations into supernovae

not only deepen our understanding of stellar physics, but

also reveal information about the broader structure of

the universe.

SN 2019hyk is located in the galaxy IC 4397, a type Sbc

galaxy with an absolute magnitude of 13.2 [2]. Discovered

in 1889 in the Bootes constellation, the galaxy is about

203 million light years away from Earth [3].

We used both spectroscopy and photometry to

study the supernova. The former provides information

on emission and absorption lines, and the latter provides

instrumental magnitudes. After color-correcting instrumental

magnitudes to apparent magnitudes, we created

a light curve which provides information on the supernova’s

classification when compared to models of known


The current system for the classification of supernovae

was established in 1940 by Fritz Zwicky and Walter Baade

in the 1940s, and it is beginning to show signs of old age.

The discovery of superluminous supernovae (SLSN) and

their awkward subdivisions under Zwicky and Baade’s

classification system show that this system is outdated.

The different classifications of supernovae do not have

clear relations with each other. In order to remake the

current supernova classification system, it is imperative

that we study more supernovae [5]. To this end, our team

decided to study SN 2019hyk.

Figure 1: SN 2019hyk

SkyShot Autumn 2020

II. Methodology

A. Photometry and Observations

The photometric analysis of SN 2019hyk consists of three

steps: telescopic observing, raw image processing, and leastsquares

fitting color calibration.

We took images with the Leitner Family Observatory’s

16-inch Ritchey-Chretien Telescope, as well as remote telescopes

from iTelescope.net. We used a STL1001E CCD camera

with the 16 inch telescope for all our data except on our

last day of observing, when we used the STT1603ME CCD

camera. To slew the telescope, calibrate, focus, and take images,

we used the SkyX program.

Arcturus was our pointing calibration star throughout the

observations, and we used Tpoint pointing models, with at

least two stars, each session. After checking the position of

Arcturus and making a new pointing model if needed, we

slewed to the supernova. Here, we checked that the star field

was correct and the telescope focused. If we needed to adjust

the focus, we used the RCOS TCC control app to move the

secondary mirror. We took series of images using Johnson V

and Johnson R filters with 1x1 binning and exposure times

of 120 seconds each. We also used remote telescope T21 in

New Mexico to take images of SN 2019hyk in Johnson V and

Johnson R filters with 2x2 binning, for exposure times of 120


We processed our images using the software MaxIm DL.

We first flat-fielded our images, and then aligned our images

using auto star matching with bicubic resampling enabled.

We then median combined the images taken in the same filter

and adjusted their light histograms in order to make SN

2019hyk appear with the greatest resolution and contrast.

Using software from Astrometry.net, we plate-solved our

combined V and R filter images to acquire sky coordinates.

Considering that the supernova is visibly distinct from

its galaxy, and little light from the galaxy interferes with the

light captured by our supernova apertures, we elected to not

perform galaxy subtraction. Doing so would vertically shift

our light curve, likely by a marginal amount, and not affect

the shape or size of the curve.

B. Photometric Analysis

We programmed an automated pipeline that extracts

useful information from the combined images to calculate

the standard magnitude of SN 2019hyk. The program first

measures the flux of the supernova by summing up the

pixel values passing through a 14-pixel circular aperture,

and subtracting the average background noise collected by

a 20-pixel annulus. The flux of the supernova is related to

its instrumental magnitude through:

where m is the instrumental magnitude, and b is the

flux. Using this relationship, we can calculate the instrumental

magnitude of the supernova in each combined

image. However, to acquire the supernova’s standard magnitude,

we must also perform a color calibration on the instrumental

magnitudes. After a cross-examination of the

supernova’s magnitudes in both V filter and R filter, we can

correct for the linear shift in magnitudes caused by the

CCD’s color biases.

To perform a linear transform on our instrumental magnitudes,

we need the instrumental magnitudes of the supernova

in both V and R filters. After comparing them to

the standard magnitudes, we perform two least-squares

fittings to acquire the transformation coefficients:

where v and r are instrumental magnitudes, V and R are

standard magnitudes, and Tvr,Cvr,Tv and Cv are the coefficients

we are interested in. The pipeline was fed with

the coordinates, as well as V and R standard magnitudes of

30 calibration stars from APASS (The AAVSO Photometric

All-Sky Survey), as shown in Figure 3.

These calibration stars provide a general correlation

between color and instrumental magnitude. The pipeline

locates these calibration stars using the WCS files of

solve-fielded images and then measures their instrumental

magnitudes with the same aperture and annulus. It then

uses least-squares fitting to calculate the values of transformation

coefficients by making a V - R vs v - r plot and a

V - v vs V - R plot. The color calibration coefficients enabled

us to derive the standard magnitude of SN 2019hyk and

generate a light curve, upon which we could determine the

supernova’s type through a model fitting.

Figure 2: Observation Schedule


SkyShot Autumn 2020

III. Results

The instrumental magnitudes v and r, color-calibrated

apparent magnitudes V and R, and errors of V are included

in the table below.

Figure 4: Instrumental and Apparent Magnitudes

Figure 3: 30 APASS Calibration Stars in the

Vicinity of SN 2019hyk

C. Spectroscopy

We took spectra of the supernova using a DSS7 spectrometer

on the 20-inch reflector at the Hotchkiss School

to perform spectroscopy. To minimize background noises

and light pollution, we subtracted the sky image from the

supernova image. We then performed a linear fit on a calibration

spectrum of Arcturus to an archive spectrum to derive

the corresponding wavelengths. Finally, we transformed

the spectrum of the supernova using the redshift of its host

galaxy. Using the formula:

Figure 5 is the color-calibrated light curve for SN

2019hyk in V band. Six apparent magnitudes are plotted

against days since peak magnitude, which occurred on

June 27, 2019, according to our model. With a steady plateau,

the light curve clearly matches the model of a Type

II-P supernova despite the relatively large uncertainties in

the second and last measurements. The most recent observations

indicate that the supernova will continue to

dim in the following month.

This derives a plot of our spectrum that indicates the supernova’s


D. Error Propagation

We created covariance matrices for each filter’s linear fit

process to calculate the uncertainty of our measurements of

SN 2019hyk’s V and R magnitudes. We then diagonalized

each matrix and took their square roots. From this process,

we obtained two values for each linear fit: the slope uncertainty

and the offset uncertainty. We then plugged these uncertainties

into our linear fit line equations in order to find

the maximum and minimum V and R magnitudes possible

with those uncertainties. These minimum and maximum

magnitudes formed the endpoints of our error bars on our

SN 2019hyk error curve. As the error involved in measuring

the flux of SN 2019hyk and our calibration stars was negligible,

we decided not to include it in our uncertainty calculations.

Figure 5: V Band Light Curve with Type II-P Super-

Figure 6 shows both the V and R light curve of SN

2019hyk. The R magnitudes are generally brighter than the

V magnitudes, with the exception of the second and last

measurements. Just like most supernovae, R magnitude

changes following the trend of V magnitude.

Figure 7 is a spectrum of SN 2019hyk taken by the 20-

inch reflector at the Hotchkiss School. Due to the limited

aperture size, exposure time, and the supernova’s decreas-


SkyShot Autumn 2020

ing brightness, there is a lot of noise in the spectrum, even after

sky subtraction. However, it can still be clearly seen that the spectrum

peaks at 656nm, the wavelength of the Hα emission line.

This spectral identity corresponds to the photometric measurements

and further proves SN 2019hyk to be a Type II supernova.

IV. Conclusions

Our research on SN 2019hyk contributes to a more

complete understanding of supernovae and their extragalactic

significance. More specifically, we can use the spectrum

and light curve to identify aspects of the origin and

composition of SN 2019hyk.

SN 2019hyk is a type II-P supernova. This is evident

from both the Hα emission line in its spectrum and the

prolonged period of high luminosity ‘plateau’ following

this peak. SN 2019hyk is the result of the collapse of a giant

star, roughly eight to fifty times as massive as our sun.

Such a star would have passed the silicon burning process

and released abundant heavy elements as it went supernova,

creating the cradle for newborn stars and planets.

Figure 6: R Band Light Curve with Type II-P Supernova

Going forward, we would like to continue taking V and

R images of SN 2019hyk using remote telescopes to prolong

our light curve. If possible, we would also like to take

B images of SN 2019hyk in order to obtain more accurate

magnitude information. Later on, we will move on to other

newly-discovered supernovae and study their spectra

and light curves. Our data, along with the supernova data

from observers worldwide, will help astronomers to gain

a better understanding of supernova physics and the star

forming process.


Thank you to the following people for assisting us with

our research on SN 2019hyk: Dr. Michael Faison, Michael

Warrener, Ava Polzin, Trustin Henderson, and Imad Pasha.


Figure 7: Spectrum of SN 2019hyk with Hα Emission Line

Several of our images were taken on days with poor seeing

and light cloud cover, which increased the noise in our images.

The images taken on July 14th are poorly focused. Additionally,

our images from July 27th were taken with the STT1603ME CCD

camera instead of our usual STL1001E CCD camera. For extraneous

reasons, we could not take flats for these images. This further

increased the noise present in our images and increased our uncertainties.

[1] Stanek, K.~Z. 2019, Transient Name Server Discovery

Report 2019-1053

[2] Seligman, 2019, Index Catalog Objects: IC 4350 -


[3] Virtual Telescope, 2019, Supernova SN 2019hyk in

the Spiral Galaxy IC-4397

[4] Weizmann, 2019, SN 2019hyk Transient Name Server

[5] Stevenson D. S, 2014, Extreme Explosions Supernovae,

Hypernovae, Magnetars, and Other Unusual Cosmic

Blasts, Springer, New York


On Method



SkyShot Autumn 2020


SkyShot Autumn 2020

s of Discovering


nesh Nagpal

Artist concept of the TRAPPIST-1 planetary system. Source: NASA, 2018 17

SkyShot Autumn 2020

We are currently in the midst of a golden age of planet

discovery, a burst that has come along quite rapidly. Even

as little as twenty-five years ago, the question of whether

there existed planets orbiting other stars like our Sun

remained open. However, in 1995, a team led by Michel

Mayor and Didier Queloz at the University of Geneva

achieved the first definitive detection of an exoplanet

in orbit around a main-sequence star: 51 Pegasi B. In

the years since, exoplanet science has come leaps and

bounds. Through the efforts of scientists worldwide and

large-scale endeavours such as the Kepler Space Telescope,

we now know of over 4000 confirmed exoplanets

in more than 3000 unique stellar systems—and we’ve

barely scratched the surface. But how on Earth do we

find these planets?

Today, many methods for discovering exoplanets

exist, of which two have experienced the most success:

transits and radial velocities. The first of these, the transit

method, works by monitoring the brightness of stars

over time. A planet passing between us and a star will

block a small fraction of its star’s light, which will manifest

a dip in the star’s brightness as we observe it. Observing

such dips in a star’s lightcurve is a telltale piece

of evidence for the presence of planets orbiting it. The

transit method is most sensitive to short period planets

with large radii, since we are more likely to both catch

such planets in a transit and detect the resultant dip in

the star’s lightcurve. Despite this method’s limitations

with regards to detecting longer period planets, missions

such as the Kepler Space Telescope have phenomenally

exploited this technique. Kepler alone managed to find

2662 exoplanets, more than half of all those currently


The second of these techniques is called the radial velocity

method. Whenever two bodies form a gravitationally

bound system, each settles into orbit around their common

centre of mass (COM). In most planetary systems,

the host star’s mass far outweighs that of any planets that

may orbit it, causing the COM’s location to lie very close

to the star. In our case, the COM of the Earth-Sun system

lies within the Sun’s outer layers! As a consequence, our

orbital motion around the COM is much more apparent

than the Sun’s, which is perhaps best described as a periodic


While such wobbles are invisible to the naked eye and

usually too small to cause a perceptible difference in a

star’s position on an image, we can detect them by examining

a star’s spectrum. Applying the Doppler Effect to

spectral lines (Figure 1) makes it possible to measure the

star’s radial velocity, which is the component of a star’s

velocity pointing directly along our line of sight. Periodic

trends in a star’s radial velocity (RV) can indicate the presence

of an additional object—whether that be a planet

or stellar companion—that causes the observed motion.

These trends allow us to, through their gravitational influence,

detect planets or faint stellar companions. It is

then possible to use the data contained within the radial

velocity trend to investigate the nature of the companion’s

orbit—a procedure known as orbit fitting. Orbit fitting

using radial velocities can provide useful information on

a companion’s orbital period, eccentricity and even mass.

However, since RVs only tell us about the component of


Figure 1: Doppler shift observable due to the motion of orbiting bodies.

SkyShot Autumn 2020

the star’s motion aligned with our line of sight, orbit fitting

using RVs alone suffers from a degeneracy between planet

mass (M) and orbital inclination (i), the angle by which the

companion’s orbit is tilted relative to our line of sight. Thus,

it is impossible to determine planet masses using radial velocities

alone. What we can compute is the product of the

planet mass and its orbital inclination: M(sin(i)). Much like

the transit method, the radial velocity method works best

for massive, short period planets as these planets generate

a higher amplitude radial velocity signal. Additionally, short

periods make it possible to precisely fit the planet’s orbit and

then subsequently confirm model predictions using future

measurements. However, unlike transits, radial velocities

maintain their usefulness even when applied to searches for

planets located further out from their home star, since radial

velocity measurements taken over a long baseline make it

possible to study longer term trends. Still, extracting information

about the nature of planets with orbital periods of

a few decades (like Neptune and Uranus, the outer gas giants

of the solar system) remains difficult. It is in this regime

that a technique that may initially seem the most obvious of

them all can help: directly imaging the exoplanet.

Image 1

When considering techniques that could be used to discover

exoplanets, a natural thought to come to mind is the

idea of simply imaging the system in question to look for

planets directly. In practice, however, this sort of direct imaging

remains highly difficult. Stars are usually much brighter

than their planets, a property which results in them simply

drowning out the light we receive from any planets that

may lie in orbit. However, the last few years have seen great

progress in direct imaging endeavors, an exciting result of

which is the image of HR 8799 (Image 1). In the image, the

black circle in the centre is the result of a technique known

as coronagraphy, which blocks the light from the central

star, allowing the light from the four planets in orbit to be

more amenable to detection. The planet visible closest in is

thought to have an orbit of around 45 years, while the planet

furthest out orbits around once every 460 years. These are

the sort of long period planets that transits and radial velocities

struggle with. Direct imaging, on the other hand,

thrives for exactly these kinds of planets!

When we image stars using telescopes, the nature of how

their light spreads out on the detector is determined by

its point spread function (PSF). The brightness of the PSF

drops off with distance from the centre of the light distribution.

Thus, in trying to image planets widely separated

from their host stars, we have to contend with less of the

star’s contaminating light. This makes long period planets

most amenable to direct imaging. The ability of direct imaging

to look for long period planets complements the effectiveness

of transits and radial velocities at characterising

shorter period planets! Furthermore, direct imaging can be

used synergistically with radial velocities to determine the

actual masses of planets rather than just M(sin(i))! The additional

information provided by jointly using radial velocities

and direct imaging can help us pin down the orbits

and physical properties of long period planets to a much

greater precision. While direct imaging is still in its relative

infancy as a technique, the rapidly advancing nature of the

field as well as the planned capabilities of next generation

missions, such as the Nancy Roman Space Telescope, paint

a bright picture for the future of exoplanet science. By

making exoplanets at wide separations amenable for study

and characterisation, direct imaging when used together

with other techniques such as radial velocities, will allow

us to gain a better picture of the population of planets that

exist in our galaxy.


[1] [@NobelPrize]. (2019, October 8). The method used

by research groups to find a planet is called the radial velocity

method; it measures the movement of the host

star as it is affected by the gravity of its planet. Retrieved

from Twitter at https://twitter.com/NobelPrize/status/1181550417588768768/photo/1.

[3] Kaufman, M. (2017, January 26). A four-planet system

in orbit, directly imaged and remarkable. NASA Exoplanet

Exploration. https://exoplanets.nasa.gov/news/1404/afour-planet-system-in-orbit-directly-imaged-and-remarkable/.


SkyShot Autumn 2020

Astrophotography on the

Cheap: A Guide

Cameron Woo

If you’re like me, you started in normal DSLR photography,

and were drawn into astrophotography by seeing

beautiful landscape shots of the Milky Way and wanted

to do it on your own. Well you probably can’t do those

because light pollution is everywhere (especially on the

east coast of the US). But that doesn’t mean you can’t get

beautiful astrophotography images. So here’s a guide for

astrophotography with just a camera, lens, and tripod.


People like to say that it’s not the tool, but how you

use it. But that’s only true to an extent. For astrophotography,

you need to collect a lot of light on your sensor

(because these objects are dim). So you will need a camera

that allows you to shoot long exposures with manual

control. If you’re going from normal photography

to astrophotography, that likely isn’t an issue. It means

using a DSLR or mirrorless camera. (Or a dedicated astronomy

camera, but if you’re already using that then

you probably don’t need this guide).

You will also need a tripod. This is pretty non-negotiable

because you need a stable surface to mount your

camera onto. Any shakes (even the slightest vibrations

from pressing the shutter button) WILL make your image

shaky and give you streaking stars.

You can use whatever lens you want. For beginners,

typically wider and faster is better. Faster, meaning a

lower f-stop or wider aperture. This allows the camera

to collect more light in less time. A wider lens also allows

you to take longer exposures with less noticeable

star trailing. There are online calculators, but I use the

500 rule to estimate an exposure length, then adjust

based on my images. The 500 rule states that the maximum

exposure time one should use is equal to 500/focal


For “cheap” this equipment may run you around $300

for a camera, $100 for a tripod, and $300 for a lens, if you

buy all new. So the used/refurbished market is a good

place. However, many people even attempting this likely

already have a camera and lens and tripod. If you’re

fresh in, you should probably definitely get a star tracker,

but that’s a different tutorial.


The key to getting good images is to take many photos

of the same object and stack them in a program like

Deep Sky Stacker (in Windows) or SiriL (in MacOS).

There are four types of images (aka frames) that you

should take: lights, darks, flats, and bias/offset frames.

Note that all these images should be taken in a RAW file

format with noise reduction turned off. RAW allows

us to get every piece of data that the camera collects,

unmodified. Noise reduction is achieved through collecting

lots of data and calibration frames, so the camera’s

internal noise reduction is unnecessary and possibly

harmful to your final image.

Where to Shoot/Bortle Scale

Although this guide is meant for people living in

heavily light polluted skies, that doesn’t mean you can’t

use similar methods if you are lucky enough to drive to

darker skies. The most common way to determine how

dark your sky is is by using the Bortle scale. The scale

ranges from 1-9, with 9 being the most light polluted,

city sky at 1 being a truly dark sky, far far away from light

pollution. There are many light pollution maps that can

give you an idea for your Bortle scale, such as darksitefinder.com.

These can also help you find any close areas

that may be slightly better. Apps like Clear Outside can

give you an actual number, but it can sometimes be inaccurate.

It tells me my town is a Bortle 6, but the town

right over is a Bortle 8 - and a mile difference isn’t going

to drop your class by 2 levels, so be wary. The best way

to assess your conditions is to go outside and look up! In

the end, it won’t matter if the app says you live in Bortle

3 skies if your town just installed fancy, new, bright, blue

LED street lamps in your cul de sac.


These are actual images of the object. For these, you


SkyShot Autumn 2020

use as wide an aperture as you can, a slow

shutter speed, and a high ISO. There are,

of course, downsides to these decisions.

A wide aperture can lead to distorted,

T shaped stars in the corners of the image.

These can be avoided by stopping

down the lens, though you sacrifice how

much light you collect. It’s up to you to

play around and find the balance. If you

need light, you can always crop the corners


A slow shutter speed may introduce

star trails, which we absolutely don’t

want. So you must find a balance, using

the 500 rule.

High ISO creates a very noisy image.

cap on. These must be shot at the same

ISO, shutter speed, as your lights and

when the sensor is at the same temperature.

This means they should be taken

in between lights. You only need about

20 dark frames, though it never hurts to

take more. They must be taken at every

imaging session. These dark frames create

a base noise pattern that will be removed

from your stacked image.


Flat frames are flat white pictures

that help remove lens distortion like vignetting.

They must be taken with the

same lens and aperture settings as your

These are similar to dark frames and

are meant to remove the base noise pattern

present inherently in the sensor.

These frames should be taken at the

fastest shutter speed possible, and at the

same ISO as your lights. Aperture doesn’t

matter. Take 10-20 bias frames.

And that’s all the frames you need. Image

stacking and processing is a whole

other tutorial, so I’m not going to mention

that here.

When and What to Image

Living in light pollution is a real

bummer, and may discourage you from

shooting (especially when you take a

photo and see a bright grey sky show up

in your photo), but we can still make the

most of the night sky. Apps like Clear

Outside and websites like Clear Dark Sky

will let you see detailed cloud cover and

seeing/transparency conditions, which

can drastically change the quality of

your shots. Shooting high at the zenith

(straight up) will let you shoot through

the least amount of light pollution and

atmosphere, so pick targets high in the

sky. Also, choose large, bright targetsthe

Orion Nebula is a great example. It’s

bright and easy to find in the sword of

Orion. And remember that a full moon

will produce a TON of light pollution, so

schedule your shooting around the new

moon. Also, use planetarium software

like Stellarium to plan your shots and

explore new targets.

Good luck and clear skies!

An example of a Stellarium view.

Preferably, we’d use a lower ISO, but

since we aren’t using a star tracker we

need a high ISO. Once again, find a balance

between light and noise, but know

that much noise will be removed in processing.

The more lights you have, the more

images you have to play around with and

stack. So take as many lights as possible.

In Bortle 8 skies, I like to take at least 70.


These are images, but with the lens

lights. One popular method is the “white

T-shirt method”, where you take a white

T-shirt, stretch it over the lens, and point

it at the sky at dusk or dawn. We want to

make the frame as evenly lit as possible,

so the sky is a nice, large, diffused light

source. Take about 10-20 flats. These

frames are most easily taken in aperture

priority mode. This way you know you’re

collecting enough light.



SkyShot Autumn 2020

Tulip Nebula (Sh2-101)

© Ryan Caputo

The Horsehead Nebula (Barnard 33)

© Wilson Zheng


The Orion Nebula (Messier 42)

© Cameron Woo

SkyShot Autumn 2020

Milky Way Galaxy over Etscorn Campus Observatory at New Mexico Tech

© Owen Mitchell

The Moon

© Nathan Sunbury

The Pleiades (Messier 45)

© Cameron Woo


SkyShot Autumn 2020

The Sunflower Galaxy (Messier 63)

© Wilson Zheng

The Ring Nebula (Messier 57)

© Nathan Sunbury

The Orion Nebula (Messier 42) and Running Man Nebula (Sh2-279)

© Jonah Rolfness


SkyShot Autumn 2020

Milky Way Galaxy, as seen from Kaanapali in Maui

© Cameron Woo

The Pinwheel Galaxy (Messier 101)

© Jonah Rolfness

Sadr Region

© Jonah Rolfness


SkyShot Autumn 2020

The Summer Triangle Asterism

(Deneb-Cygnus, Vega-Lyra, Altair-Aquila)

© Cameron Woo

The Hercules Cluster (Abell 2151)

© Ryan Caputo

Star Trails Over Pierson College

© Anavi Uppal


SkyShot Autumn 2020

Messier 3 (near Bootes)

© Wilson Zheng

The Heart Nebula (IC 1805) - Fish Head Nebula (IC 1795) and Melotte 15 Mosaic

© Jonah Rolfness


SkyShot Autumn 2020

Removing Noise and Improving

the Quality of Astronomical

Observations with Fourier


Ezgi Zeren

Dated: 15 September 2019


In this research, the quality of astronomical images was improved by eliminating the imperfections possibly

caused by sky conditions or disturbances to a telescope. Using Fourier series and transforms, the images

were processed in MATLAB to remove clouds, blur, smear, and vignetting. After Fourier-transforming the

original images, a filter was multiplied with the Fourier transform of the images. Then, the inverse

Fourier transform process was performed to obtain the filtered images. A high-pass sharp cut-off filter was

used to emphasize the edges of astronomical images and to get rid of blur. In order to remove clouds, smear,

and vignetting, a high-pass Gaussian filter was applied to the images. The resultant filtered images

suggested an improvement in the image quality and displayed more distinguishable celestial objects.

PACS numbers: 02.30.Nw, 06.30.−k, 07.90.+c, 95.85.−e

Keywords: Astronomy, Fourier transforms, high-pass Gaussian filter, high-pass sharp cut-off filter, MATLAB


I. Introduction

High-quality astronomical images are essential to the

discovery of our universe. Astronomers try to make their

images as clear and accurate as possible by using lossless

file formats and processing the images. For more than

four decades, astronomers have been using the Flexible

Image Transport System (FITS) to interchange as much

data as possible with lossless compression. FITS was so

successful that even scientists started using it in digitizing

manuscripts and medical imaging [1]. After taking images

with a professional telescope, astronomers use image

processing to improve the quality of their images and the

accuracy of their measurements.

Even though image reduction is a well-developed technique

to increase image quality, there are obstacles that

sometimes prevent astronomers from obtaining the images

they need, such as sky conditions and other disturbances

from the environment. It may not be possible to

change the sky conditions at a certain night or prevent

the vibrations coming from the floor, but images taken

at these circumstances can be improved to an extent that

they are available for use in procedures that need precise

measurements such as the orbit determination of celestial


The data used in this research includes images taken

with a 20-inch CCD reflecting telescope in the Sommers-Bausch

Observatory at the University of Colorado

Boulder as well as other images from astronomers around

the world. The images included light cloud coverage, blur,

smear, and vignetting. The impurities in the images were

eliminated by applying a high-pass Gaussian filter or a

high-pass sharp cut-off filter created in MATLAB, using

Fourier series and transforms. Since the impurities were

light, they produced a low-frequency noise, which made

it possible to eliminate them without damaging the necessary


II. Data Acquisition

A. Observations and Image Reduction

The image of Saturn, FIG. 9, was obtained using the

PlaneWave Telescopes in the Sommers-Bausch Observatory

at the University of Colorado Boulder [2]. The

observatory was located at a longitude of 105.2630 W, a

SkyShot Autumn 2020

Every function can be expressed as a waveform that is

composed of sines and cosines. Since humans became very

successful at horology [10], the study of time measurelatitude

of 40.0037 N, and an altitude of 1653 meters. The two

20-inch (508 mm) telescopes, Artemis and Apollo, were CDK20

Corrected Dall-Kirkham Astrograph carbon-fiber truss telescopes

and had a focal length of 3454 mm and a focal ratio of

f/6.8. Without any off-axis coma, astigmatism, and field curvature,

the CDK20 telescopes had a 52 mm field of view. They

also used CCD software for imaging.

A Bahtinov Mask [3], a device that is used for achieving a

high-level accuracy when focusing, was attached to focus the

telescope during observations, and the telescope was slewed to

a star of magnitude 2 to 4. After focusing as precisely as possible,

the Bahtinov Mask was removed, and the telescope was

slewed to Saturn. Then, a test image was taken to ensure that

there is no issue with focusing and that the telescope is slewed

to the correct right ascension and declination. In order to do

image reduction, three types of images were taken with the

telescopes: light frames, flat frames, and dark frames [4].

Dark frames were the images that contain no light, and

they were used to eliminate the effect of having different

signal readings from the camera sensors. FIG. 3 shows an

example of a dark frame. The exposure time for the images

was 70 seconds in all of the observations, since it was

enough to have a high-quality image.

Figure 3: An example dark frame.

The image reduction was done with AstroImageJ, an

open source image processing software. The dark frames

were subtracted from the light frames and the subtracted

image was divided by a normalized version of the flat

frames. This process made the reduced images of Saturn

almost free of all imperfections that are caused by the telescopes

and the imaging CCD arrays. Thus, the only problem

was the blur caused by the resolution of the telescopes.

Figure 1: An example light frame.

Light frames were the frames that contained the image of the

celestial bodies in the sky. If it is not possible to perform image

reduction, astronomers only use the light frames as their data,

because the only necessary data about the celestial objects is

stored in the light frames. Other frames are only used to further

improve the quality of the images by eliminating possible imperfections

caused by the equipment. FIG. 1 shows an example

of a light frame, an image of stars in the sky.

Flat frames were the frames that only contain an image of a

white wall, and that were used to eliminate the optical imperfections

such as the effect of having dust on the sensors. FIG. 2

shows an example of a flat frame.

Figure 2: An

example flat


B. Data from Other Astronomers

The image of the Eagle Nebula, the Pillars of Creation,

FIG.4, was taken by Professor Peter Coppinger [5], Associate

Professor of Biology at Rose-Hulman Institute of Technology,

who is an amatuer astrophotograher.

The image with the light cloud coverage, FIG. 14, was

taken by Rob Pettengil [6] with the Sony RX100V camera

at ISO 400, with a focal ratio of f/2 and a focal length of 24

mm. The exposure time was 2 minutes.

The image with smearing, FIG. 24, was taken by Mike

Dodd, Louise Kurylo, Michelle Dodd and Miranda Dodd at

the Hidden Creek Observatory [7] with a Astro-Tech AT-

130EDT refractor, which has an aperture of 130 mm (5.12”),

a focal length of 910 mm, and a focal ratio of f/7.

The image with vignetting, FIG.19, was taken by Jerry

Lodriguss [8] with a 12.5 inch (317.5 mm) Dobsonian Telescope

that has a focal ratio of f/6 and a focal length of 75

inches (1905 mm).

III. Methods

A. The Fourier Transform [9]


SkyShot Autumn 2020

ment, scientists have been trying to use frequencies in their

experiments for better accuracy. The Fourier Transform is

a useful tool that describes a given waveform as the sum of

sinusoids of different frequencies, amplitudes, and phases.

If a function Φ(p) has a Fourier Transform F(x), then Φ(p) is

also the Fourier Transform of F(x). This “Fourier Pair” , Φ(p)

and F(x) is defined as

C. The Two-Dimensional DFT

In this research, images will be Fourier transformed.

Thus, a two-dimensional Discrete Fourier Transform is

done with the FFT algorithm.

Instead of the formulae for the one-dimensional case,

Eq. (3) and Eq. (4), the two-dimensional DFT has the formulae

B. The Fast Fourier Transform Algorithm (FFT) and

the Discrete Fourier Transform (DFT) [11]

The Discrete Fourier Transform (DFT) is the mutual transformation

of a pair of sets, [an] and [Am], such that each contain

N elements. The formulae for DFT are

where m and n are the rows and columns of g[m,n], and

j and k are the rows and columns of G[j,k] columns. The

Fourier Transform Matrix G[j,k] is obtained after Fourier

transforming the data matrix g[m,n]. Then, a filter can

be applied to G[j,k], and the filtered g[m,n] matrix can be

found by the inverse DFT synthesis process, where the input

matrix is the filtered G[j,k] as in Eq. (6).

D. Using MATLAB for Fourier Transforming FITS,

png, and jpg Images


, the formulae for DFT can be set out as a

matrix operation as follows.

As shown in section III. A., the Fourier transformation

process involves integral calculus which can cause a problem

when the integrand is an experimental data set instead of an

analytical function. The Fast Fourier Transform algorithm

(FFT) was discovered to deal with complex integrands like

these. This algorithm reads the data into an array and returns

the transformed data points.

The matrix operation above requires N2 multiplications.

However, the fast Fourier transform (FFT) reduces

the number of multiplications necessary from N 2 to about


As mentioned in section I, FITS images were specifically

tailored for Astronomy, and they have a special but expanding

usage amongst the scientific community. Therefore,

the image processing functions in the Image Processing

Toolbox of MATLAB, are not capable of processing FITS

files. However, MATLAB has a special function to read

FITS files, called fitsread() [12]. After reading the FITS files,

with fitsread(), the imagesc() function was used to show

the image because imagesc() displays the image with a

scaled data. Then, the colormap was set to be gray rather

than the default color, blue, for aesthetic purposes.

For png and jpg images, imread() function was used to

read the files. Then, the images were converted to gray

scale with rgb2gray() function. For displaying the gray image,

imshow() function was used.


SkyShot Autumn 2020

MATLAB has its own two-dimensional Fast Fourier Transform

function called fft2(). This function, along with the fftshift() function

was used to place the low frequencies in the center of the

image. Since it was difficult to visualize complex numbers, the

magnitudes were displayed as a matrix. Also, having the sum of

the values in the center might have caused other magnitudes to

be dwarfed when compared to the center. This was prevented by

taking the logarithm of the values. The logarithm was taken in

the form of log(1 + matrix element value) to avoid the possibility

of having negative results when the matrix element value is

less than 1. The subroutine fftshow.m, written by Alasdair McAndrew

of Victoria University of Technology, was used to display the

two-dimensional DFT. The subroutine can be found in the appendix.

The command impixelinfo displays pixel information such as

the pixel value, which is helpful to check that nothing goes wrong

with the Fourier transform.

E. Edge Detection of the Images of Nebulae Using MATLAB

The identification of a nebula against the sky can be challenging

when the image taken has a significant amount of noise, which is

usually caused by sky conditions such as air pollution. There is

noise in the image of the Eagle Nebula, the Pillars of Creation, FIG.

4, which makes it difficult to see the nebula’s original shape. The

image also contains a slight amount of vignetting, which causes

the image to lose details in the corners and edges of the picture.

Figure 5: Fourier transform of the Eagle Nebula.

The high-pass sharp cut-off filter has the pixel value of

0 in the center and the pixel value of 1 everywhere else, in

order to block the low frequencies and only pass the high

frequencies when multiplied with the Fourier transform.

The high-pass sharp cut-off filter was created in the shape

of a circle in order to block the desired low frequencies in

two dimensions. Since the filter was multiplied with the

Fourier transform, the size of the meshgrid was set to be

same with the original image, 541x542. The equation of

a circle was used to create a circle, and the radius of the

circle was set to be 0.2 pixels because after trying several

radii, 0.2 was determined to be functioning well for edge

detection. The code for creating the filter is below.

Since the radius of the filter was 0.2 pixels, the filter appears

to be the shape of a square instead of a circle, and

the filter is hard to see in the filtered Fourier transform,

FIG. 7; however, 0.2 pixels was enough for edge detection.

Figure 4: Original image of the Eagle Nebula.

After performing the Fourier transform with the code in section

III. D., FIG. 5 was obtained. The image of the Fourier transform

shows the low frequencies in the center and high frequencies

when moving away from the center. Since the noise has a low

frequency, a high pass sharp cut-off filter was used for edge-detection.

Figure 6: The high-pass sharp cut-off filter for the



SkyShot Autumn 2020

The filter and the Fourier transform were multiplied in order

to eliminate the low frequencies and emphasize the high

frequencies. The filtered Fourier transform, FIG. 7, was set

to grayscale since it was making it easier to compare the filtered

Fourier transform from the original Fourier transform.

After the application of the filter, the edges of the Eagle

Nebula became clearer to the observer, meaning that the

high-pass sharp cut-off filter was successful at performing

edge detection on the image of the nebula.

F. Edge Detection of the Rings of Saturn Using


Even after processing the astronomical images with image

reduction and using the highest quality way to store

the images with FITS files, astronomical images might not

be clear enough. There is always a limit of resolution that

a certain telescope can reach. The image of Saturn, FIG. 9,

was reduced with the method described in section II. A.

However, the rings of Saturn were not clear enough because

of the restriction of the limits of resolution of the

telescope [13].

Figure 7: The filtered Fourier transform of the nebula.

Finally, the inverse Fourier transform was applied to the

filtered Fourier transform, FIG. 7, using the ifft2() function

of MATLAB. The ifft2() function is for two dimensional inversion

Fourier transformation, which is necessary to finalize

the filtering of the images used in this research.

Figure 9: The original image of Saturn.

Since the image of Saturn was stored in a FITS file, the

code in section III.D. for FITS images was used, and the

Fourier transform was performed in the same way described

in section III.F.

Figure 8 displays the image of the Eagle Nebula after the

high-pass edge detection filter was applied.

Figure 8: The filtered image of the nebula.

Figure 10: The Fourier transform of Saturn.


SkyShot Autumn 2020

Since the determination of the rings of Saturn requires edge

detection, a high-pass sharp cut-off filter was used for the image

of Saturn as well. However, the original image size and the radius

of the filter were much bigger.

Figure 13: The filtered image of Saturn.

E. Edge Detection of the Images of Nebulae Using


Figure 11: The high-pass sharp cut-off filter for Saturn.

The high-pass filter was multiplied with the Fourier transform

to produce the filtered Fourier transform in the same way described

in section III.F. Since the radius of the filter was 20 pixels,

its circular shape was more distinct this time, when compared

with the high-pass filter used for the Eagle Nebula.

Since the light cloud coverage and the blur caused by

the sky conditions in astronomical images have a from

similar to that of a Gaussian function, they can be eliminated

with a Gaussian high-pass filter [14]. FIG. 14 displays

a light cloud coverage which makes it hard to determine

the stars in the sky.

Figure 14: Original image of the light cloud coverage.

The image was read and Fourier-transformed with the

code displayed in section III.D.

Figure 12: The filtered Fourier transform of Saturn.

The Fourier-transformed image of the Eagle Nebula after the

high-pass edge detection filter is applied is displayed in FIG. 13.

After the application of the sharp cut-off filter to the image of

Saturn, the rings of Saturn became more recognizable. The rings

got darker while the background got brighter, making the edges of

the rings very clear.

Figure 15: The Fourier transform of the cloud coverage.


SkyShot Autumn 2020

The Gaussian function, Eq. (7), creates a Gaussian filter

with standard deviation σ. The standard deviation value determines

the strength of the filter, as the standard deviation

value gets higher, the filter becomes more distributed, and

when the value gets lower, the filter becomes more dense.

In order to create the high-pass Gaussian filter, the discretization

[15] of the Gaussian function was subtracted

from 1, Eq. (8), making the pixel values in the center equal to

0. The MATLAB code for Eq. (8) is provided below.

Figure 17: The filtered Fourier transform of the cloud


Figure 18 is the Fourier-transformed image of the light

cloud coverage after the high-pass Gaussian filter is applied.

The inverse Fourier transformation process was performed

as described in section III. E.

Figure 18: The filtered image with cloud coverage.

Figure 16: The high-pass Gaussian filter for

cloud coverage.

Figure 16 shows the zoomed in version of the high-pass

Gaussian filter. The smooth edges of the Gaussian filter seen

in FIG.16. are very useful for eliminating noise. In addition,

using a Gaussian filter prevents the ringing effect, a wavy

appearance near the edges of images due to the loss of high

frequency information. With images with cloud coverage,

not having a ringing effect is crucial, since the ringing effect

might also ruin the images.

As can be seen from the filtered image, FIG. 18, the

clouds are not visible to the observer anymore. This suggests

that the cloud removal with the high-pass Gaussian

filter was successful. Moreover, after the removal of the

clouds, the stars became more apparent, and their shape

became more circular, since the noise around the stars was

also eliminated.

H. Getting Rid of Vignetting in Astronomical Images


Vignetting is caused by the design of the sensor and

the lens of a camera; therefore, it is unavoidable in every

image. The light coming towards the center of a camera

sensor strikes at a right angle, but the angle of incidence

decreases further from the center of the censor, which


SkyShot Autumn 2020

causes vignetting. However, vignetting can be present in different

amounts in different images which makes its effects nearly invisible

in some images.

The image of the sky, Figure 19, has a visible amount of vignetting

in the corners and edges, and a slight noise in the center, which

ruins the clarity of the image. Since it is light enough, the noise in

the center can be eliminated, using a high-pass Gaussian filter. After

eliminating the noise, the sky will be more visible and the uneven

distribution of the light due to vignetting will be eliminated.

Figure 19: The sky image with vignetting.

The image was Fourier transformed as described in section III.

D. As can be seen from the Fourier transform of the image of the

sky, the low frequencies in the center of the image have a small radius.

Thus, a Gaussian filter with 2.5 standard deviation was used

to eliminate the noise.

Figure 21: The high-pass Gaussian filter for vignetting.

Since the standard deviation of the Gaussian filter was

small, the filter appears to be like a black point in the center

of the filtered Fourier transform of the image with vignetting,

FIG. 22. Even though it seems to be too small to

have a significant effect in the image, the filter cancels out

enough of the noise in the image.

Figure 22: The filtered Fourier transform of the image

with vignetting.

Figure 20: The Fourier transform of the image with


Figure 23 displays the Fourier-transformed image of the

Sky with vignetting after the high-pass Gaussian filter was

applied. The inverse Fourier transformation process was

performed as described in section III. E.

The resultant filtered image no longer has uneven

brightness distribution, and the edges of the image do not

have a darker color than the center of the image. Since the

noise in the center of the image was eliminated and the

effects of vignetting was canceled out, the application of

the high-pass Gaussian filter was successful.


SkyShot Autumn 2020

The image with smearing was Fourier transformed as

described in section III.D. The Fourier transform of the

image with smearing has a small radius of distinguishable

low frequencies in the center, but since the smearing was

harsher than a slight noise, a Gaussian filter with a relatively

higher standard deviation was used.

Figure 23: The filtered image with vignetting.

I. Eliminating Smearing in Astronomical Images Using


Smearing in astronomical images is usually caused by factors

independent of the telescope. If a telescope is disturbed

during its exposure time, smearing will probably occur. This

can be prevented by removing any disturbances near the

telescope. However, the vibrations from the floor can be an

unavoidable factor that causes smearing. In situations like

this, the images can be recovered by applying a high-pass

Gaussian filter to treat the smear as a noise around the actual

celestial objects.

Figure 24 shows the image of stars with smearing. Instead

of being circular, the stars seem to look like ellipses. This

image can be recovered with a high-pass Gaussian filter because

the smeared parts of the stars have a lower frequency

than the actual parts of the stars. If the smear was as light as

the stars, then recovering with a filter might not be possible.

Figure 25: The Fourier transform of the sky image with


The high-pass Gaussian filter used for the smearing,

FIG. 26, has a standard deviation of 8, which cancels out

a more distinct amount of noise from the original image.

Figure 26: The high-pass Gaussian filter for smearing.

Figure 24: The sky image with smearing.

As can be seen from the filtered Fourier transform of

the smear, Figure 27, the high-pass Gaussian filter used for

smearing is very visible in the center.


SkyShot Autumn 2020

Figure 27: The filtered Fourier transform of the smeared


FIG. 28 displays the image with smearing after the high-pass

edge detection filter is applied, and the image is Fourier-transformed.

The inverse Fourier transformation process was performed

as described in section III. E.

Since the background noise from the sky lessened clearly, the

high-pass Gaussian filter was successful at removing the noise

around the images of stars with smearing. In addition, the shape

of the stars became more circular, when compared to their initial

smeary shape. Thus, the high-pass Gaussian filter improved the

quality of the image.

Figure 28: The filtered sky image with smearing.

IV. Conclusion

In this research, astronomical images with blur, smearing, light

cloud coverage, and vignetting were processed with MATLAB.

Image processing was done with Fourier series and transforms by

creating high-pass Gaussian and sharp cut-off filters. After Fourier-transforming

the images and shifting the zero-frequency components

of the discrete Fourier transforms to the center, the noise

in the center was multiplied with the filters and eliminated.

In order to emphasize the edges of the Eagle Nebula

and to make the rings of the Saturn more clear, high-pass

sharp cut-off filters with different radii were created. The

radii of the filters were determined by trial and error.

High-pass Gaussian filters were used to cancel out the

effects of vignetting, smearing, and blur. Since the noise

created by these factors was similar to a Gaussian noise,

the high-pass Gaussian filter was successful at eliminating

a portion of the noise, making the images more clear. A

high-pass Gaussian filter was also used to cancel out the

light cloud coverage in an image of the sky. The Gaussian

filter was suitable for eliminating the clouds because the

clouds had a low frequency and blurry edges. The highpass

Gaussian filters used in different images had different

standard deviations. The best standard deviation value for

an image was again determined by trial and error. Since

the Fourier transform of the image with light cloud coverage

had more low frequencies in the center than others,

the highest value of standard deviation was used to eliminate

the light cloud coverage.

After multiplying the Fourier transforms of the images

with the filters created in MATLAB, the inverse Fourier

transform process was performed in order to obtain the

filtered image.

After the application of the high-pass sharp cut-off filter,

the original image of the Eagle Nebula, the Pillars of

Creation, Figure 4, became more evident. As can be seen

from the filtered image of the Eagle Nebula, Figure 8, the

edges of the nebula are more emphasized, and the stars

are more distinguishable from the background.

The rings of Saturn in the original image, Figure 9, were

blurry before being filtered. However, after the high-pass

sharp cut-off filter was applied, the rings appear in black

around Saturn. The rings are more recognizable in the filtered

image of Saturn, Figure 13.

The image with light cloud coverage, Figure 14, was

covered with noise, and the stars were difficult to see. After

using a high-pass Gaussian filter, the clouds, which

were light enough to be treated like a Gaussian noise, were

eliminated. As can be seen from the filtered image of the

sky, Figure 18, the stars are more distinguishable from the

background after the clouds are removed.

The sky image with vignetting, Figure 19, has uneven

brightness distribution due to vignetting. The effect of

vignetting was eliminated with a high-pass Gaussian filter.

The resultant filtered image, Figure 23, no longer has

uneven brightness distribution, and the stars are more


With the application of the high-pass Gaussian filter,

the original sky image with smearing, Figure 24, looks less

noisy. The stars in the filtered sky image with smearing,

Figure 24, are more circular, and their edges are more distinct.

This research proposed a method for the removal of

clouds, blur, smearing, and vignetting from astronomical

images with Fourier series and transforms, using MAT-

LAB. Different amounts of noise was eliminated from dif-


SkyShot Autumn 2020

ferent images, since the success of the filtering process also

depended on how much noise was present in the original

image. When compared with the original images, the resultant

filtered astronomical images suggest that the filters

worked successfully to improve the quality of the astronomical



This research was guided by the Pioneer Academics Program.

Special thanks to Professor Arthur Western for always

being a source of help and advice.

Appendix: The fftshow() Subroutine


[9] Ronald N. Bracewell, The Fourier Transform and its

Applications, International Editions, 3rd Ed., McGraw-Hill

[10] M.of Time, Mathematics, https://www.encyclopedia.com/science-and-technology/mathematics/mathematics/measurement-time

[11] J.F. James, A Student’s Guide to Fourier Transforms:

with Applications in Physics and Engineering (Cambridge

University Press, Cambridge, 1995)

[12] A. Hayes and J. Bell, http://hosting.astro.cornell.


[13] Lea, S. M. & Kellar, L. A., An algorithm to smooth

and find objects in astronomical images, (Astronomical

Journal, 1989), pp. 1238-1246

[14] Hummel, R. A., Kimia, B., and Zucker, S. W., “Deblurring

Gaussian blur. Computer Vision, Graphics, and

Image Processing,” 66–80 (1987)

[15] A. Torralba, http://6.869.csail.mit.edu/fa16/lecture/



[1] Thomas, “Learning from FITS: Limitations in use in

modern astronomical research,” Astronomy and Computing

12, 133-145 (2015)

[2] Sommers-Bausch Observatory (2018)

[3] R. Berry and J. Burnell, The Handbook of Astronomical

Image Processing (Willmann-Bell, Richmond, VA, 2011)

[4] W. Romanishin, An Introduction to Astronomical

Photometry Using CCDs, (University of Oklahoma, 2006),

pp. 79-80

[5] P. Coppinger, https://www.rose-hulman.edu/academics/faculty/coppinger-jpeter-coppinge.html

[6] R.Pettengil, http://astronomy.robpettengill.org/


[7] M. Dodd, http://astronomy.mdodd.com/flexure.html

[8] J. Lodriguss, http://www.astropix.com/html/jdigit/vi-


SkyShot Autumn 2020

Computational Astrophysics

Advancements of 2020

Priti Rangnekar

Depiction of lower error for the D3M model compared to the earlier 2LPT model [2].

Traditionally, the words “astronomy” and “astrophysics”

may conjure images of ancient star charts, telescopes staring

into the night sky, or chalkboards filled with Einstein’s

equations detailing special and general relativity. However,

with the rise of ground and space-based sky survey

projects and Citizen Science endeavors involving contributions

from amateur astronomers worldwide, the field

of astronomy is becoming increasingly data-driven and

computationally enhanced. Survey projects, such as The

Large Synoptic Survey Telescope, bring data issues such as

high volume (nearly 200 petabytes of data), large varieties

of data, and rapid speeds of data production and transmission,

requiring efficient analysis through statistical computing.

[1] As we collect more information about the physical

world and develop powerful software and hardware,

we gain the ability to methodically find patterns and make

large scale predictions based on what we do know, allowing

us to embrace the frontier of what has always been unknown.

In June 2019, researchers from institutions including

Carnegie Mellon University and the Flatiron Institute announced

the development of the first artificial intelligence

simulation of the universe - the Deep Density Displacement

Model. With the ability to complete a simulation in

less than 30 milliseconds, the model proved to be both efficient

and accurate, with relative errors of less than 10%

when compared with both accurate but slow models and

fast but less accurate models. Moreover, it could provide

accurate values for certain physical values, such as dark

matter amount, even when tested with parameters, such

as gravitational conditions, it was not originally trained

on. This is just one example of how the power of computing

techniques can allow us to better understand the universe

and its past. [2]

In 2020, research groups from around the world have

further capitalized on artificial intelligence and supercomputing

to analyze specific aspects of the universe, including

exoplanets, galaxies, hypernovae, and neutron star


Gaussian Process Classifiers for Exoplanet Validation

University of Warwick scientists Armstrong, Gamper,

and Damoulas recently capitalized on the power of machine

learning to develop a novel algorithm for confirming

the existence of exoplanets, which are planets that orbit

stars outside the Solar System. [3]

Traditionally, exoplanet surveys use large amounts of

telescope data and attempt to find evidence of an exoplanet

transit, or any sign of the planet passing between the telescope

and the star it is orbiting. This typically comes in the

form of a dip in the observed brightness of the target star,

which makes intuitive sense given that the planet would

be obstructing some light. Nevertheless, this analysis can

be prone to false positive errors, given that an observed dip

does not necessarily indicate the presence of an exoplanet;

it could also be caused by camera errors, background object

interference, or binary star systems.[3] In the case of


SkyShot Autumn 2020


a binary star system, eclipsing binaries may result, in

which a star’s brightness would vary periodically as one

passes in front of the other, causing the observed dip.

Such a phenomenon would require extended analysis

of the target star’s flux lightcurve, which shows changes

in brightness. In the case of background object interference,

a background eclipsing binary or planet

may blend with the target star, requiring researchers

to observe any offset between the target star and the

transit signal. [4]

As a result, researchers use a planetary validation

process in order to provide the statistical probability

that a transit arose from a false positive, in which

a planet was not present. [5] A common algorithm

used for validating some of the approximately 4,000

known exoplanets has been the vespa algorithm and

open source code library. The procedure, detailed in a

paper by Morton in 2012, accounts for factors such as

features of the signal, target star, follow-up observations,

and assumptions regarding field stars. [6] However,

as Armstrong, Gamper, and Damoulas explain in

their abstract published in August 2020, a catalogue

of known exoplanets should not be dependent on

one method. [5] Previous machine learning strategies

have often generated rankings for potential candidates

based on their relative likelihoods of truly being planets;

however, these approaches have not provided exact

probabilities for any given candidate. For example, in

2017, Shallue and Vanderburg developed a model that

generated rankings for potential candidates based on

their relative likelihoods of truly being planets. 98.8%

of the time, plausible planet signals in the test set were

ranked higher than false positive signals. [7]

However, a probabilistic framework is a key component

of the planetary validation process. Thus, by employing

a Gaussian Process Classifier along with other

models, the University of Warwick researchers could

find the exact statistical probability that a specific exoplanet

candidate is a false positive, not merely a relative

ranking. In general, a Gaussian Process generates

a probabilistic prediction, which allows researchers to

incorporate prior knowledge, potentially find confidence

intervals and uncertainty values, and make decisions

about refitting. [8] If the probability of a candidate

being a false positive is less than 1%, it would

be considered a validated planet by their approach.

Trained using two samples of confirmed planets and

positive samples from Kepler, the model was tested on

unconfirmed Kepler candidates and confirmed 50 new

planets with a wide range of sizes and orbital periods.


Although the computational complexity for training

the model is higher than that of traditional methods,

and certain discrepancies with vespa were found, this

approach demonstrates a clear potential for efficient

automated techniques to be applied for the classification

of future exoplanet candidates, while becoming

more accurate with each dataset due to machine learning.

In fact, the researchers aim to apply this technique

to data from the missions PLATO and TESS, which has

already identified over 2,000 potential exoplanet candidates.


Machine Learning and Deep Learning for

Galaxy Identification and Classification

Another area of artificial intelligence growing in

popularity is image classification and object detection,

with common applications for autonomous vehicles

and medical imaging. A powerful technique in this field

is a convolutional neural network, a form of deep learning

roughly based on the functionalities and structure

of the human brain. Each layer of the network serves

A depiction of an exoplanet

transit lightcurve;

the Gaussian Process

Classifier prioritizes

the ingress and egress

regions, indicated by

the 2 dotted lines, when

classifying exoplanets


An example of data

augmentation for galaxy

images using rotation

and flipping [10].

SkyShot Autumn 2020

a unique purpose, such as convolution

layers for generating feature maps from

the image, pooling layers for extracting

key features such as edges, dense layers

for combining features, and dropout layers

that prevent overfitting to the training

set. [10]

This method was applied to galaxy

classification by researchers at the National

Astronomical Observatory of Japan

(NAOJ). The Subaru Telescope, an

8.2-meter optical-infrared telescope at

Maunakea, Hawaii, serves as a robust

source of data and images of galaxies

due to its wide coverage, high resolution,

and high sensitivity. [11] In fact, earlier

this year, astronomers used Subaru Telescope

data to train an algorithm to learn

theoretical galaxy colors and search for

specific spectroscopic signatures, or

light frequency combinations. The algorithm

was used to identify galaxies in the

early stage of formation from data containing

over 40 million objects. Through

this study, a relatively young galaxy HSC

J1631+4426, breaking the previous record

for lowest oxygen abundance, was discovered.


In addition, NAOJ researchers have

been able to detect nearly 560,000 galaxies

in the images and have had access

to big data from the Subaru/Hyper

Suprime-Cam (HSC) Survey, which

contains deeper band images and has

a higher spatial resolution than images

from the Sloan Digital Sky Survey. Using

a convolutional neural network (CNN)

with 14 layers, they could classify galaxies

as either non-spirals, Z-spirals, or

S-spirals. [10]

This application presents several important

takeaways for computational

astrophysics. The first is the augmentation

of data in the training set. Since

the number of non-spiral galaxies was

significantly greater than the number of

spiral galaxies, the researchers needed

more training set images for Z-spiral and

S-spiral galaxies. In order to achieve this

result without actively acquiring new

images from scratch, they flipped, rotated,

and rescaled the existing images with

Z-spiral and S-spiral galaxies, generating

a training set with roughly similar numbers

for all types of galaxies.

Second, it is also important to note

that the accuracy levels of AI models may

reduce when working with celestial bodies

or phenomena that are rare, due to a

reduction in the size of the training set.

The galaxy classification CNN originally

achieved an accuracy of 97.5%, identifying

spirals in over 76,000 galaxies in

a testing dataset. However, this value

decreased to only 90% when the model

was trained on a set with fewer than 100

images per galaxy type, demonstrating

the potential for concerns if more rare

galaxy types were to be used.

A final important takeaway is regarding

the impact of misclassification and

differences between the training dataset

and the testing dataset. When applying

the model to the testing set of galaxy images

to classify, the model found roughly

equal numbers of S-spirals and Z-spirals.

This contrasted with the training set, in

which S-spiral galaxies were more common.

Although this may appear concerning,

as one would expect the distribution

of galaxy types to remain consistent, the

training set may have not been representative,

likely due to human selection

and visual inspection bias. In addition,

the authors point out that the criterion

of what constitutes a clear spiral is ambiguous,

and that the training set images

were classified by human eye. As a result,

while the training set only included images

that had unambiguous spirals; the

validation set may have included more

ambiguous cases, causing the model to

incorrectly classify them.

Several strategies can be used to combat

such issues in scientific machine

learning research. In terms of datasets,

possible options include creating a new,

larger training sample or employing numerical

simulations to create mock images.

On the other hand, a completely

different machine learning approach -

unsupervised learning - could be used.

Unsupervised learning would not require

humans to visually classify the

training dataset, as the learning model

would identify patterns and create classes

on its own. [10]

In fact, researchers at the Computational

Astrophysics Research Group at

the University of Santa Cruz have taken

a very similar approach to the task of

galaxy classification, focusing on galaxy

morphologies, such as amorphous elliptical

or spheroidal. Their deep learning

framework, named Morpheus, takes in

image data by astronomers and uniquely

does pixel level classification for various

features of the image, allowing it to

discern unique objects within the same

image rather than merely classifying the

image as a whole (like the models used

by the NAOJ researchers). A notable benefit

of this approach is that Morpheus

can discover galaxies by itself and would

not require as much visual inspection or

human involvement, which can be fairly

high for traditional deep learning approaches

- the NAOJ researchers worked

with a dataset that required nearly

100,000 volunteers. [13] This is crucial,

given that Morpehus could be used to

analyze very large surveys, such as the

Legacy Survey of Space and Time, which

would capture over 800 panoramic images

per night. [13]

Examples of a Hubble Space Telescope

Image and its classification results

using Morpheus [13].


SkyShot Autumn 2020

Supercomputing for Analyzing

Hypernovae and Neutron Star


Given the data-intensive nature of

this endeavor as well as the need for intensive

pixel-level classification, it is natural

to wonder how scientists are able to

run such algorithms and programs in

the first place. The answer often lies in

supercomputing, or high performance

computing (HPC). Often Supercomputers

often involve interconnected nodes

that can communicate, use a technique

called parallel processing to solve multiple

computational problems via multiple

CPUs or GPUs, and can rapidly input and

output data. [14] This makes them prime

candidates for mathematical modeling

of complex systems, data mining and

analysis, and performing operations on

matrices and vectors, which are ubiquitous

when using computing to solve

problems in physics and astronomy. [15]

The robust nature of supercomputing

was recently seen, as researchers

from the Academia Sinica’s Institute of

Astronomy and Astrophysics used the

supercomputer at the NAOJ to simulate

a hypernova, which is potentially

100 times more energetic than a supernova,

resulting from the collapse of a

highly massive star. The program simulated

timescales nearly an order of magnitude

higher than earlier simulations,

requiring significantly higher amounts

of computational power while allowing

researchers to analyze the exploding star

300 days after the start of the explosion.

[16] However, this was indeed beneficial,

as the longer timescale enabled assessment

of the decay of nickel-56. This

element is created in large amounts by

pair-instability supernovae (in which no

neutron star or black hole is left behind)

and is responsible for the visible light

that enables us to observe supernovae.

Moreover, we cannot underestimate the

importance of simulations, as astronomers

cannot rely on observations given

the rarity of hypernovae in the real

world. [17]

Supercomputers have also been used

for simulating collisions between 2

neutron stars of significantly different

masses, revealing that electromagnetic

radiation can result in addition to gravitational

waves. [18] Once again, we can

see the usefulness of computational simulations

when real observations do not

suffice. In 2019, LIGO researchers detected

a neutron star merger with 2 unequal

masses but were unable to detect

any signal of electromagnetic radiation.

Now, with the simulated signature, astronomers

may be capable of detecting

paired signals that indicate unequal neutron

star mergers. In order to conduct

the simulations using the Bridges and

Comet platforms, researchers used nearly

500 computing cores and 100 times

as much memory as typical astrophysics

simulations due to the number of physical

quantities involved. [19] Despite the

tremendous need for speed, flexibility,

and memory, supercomputers prove an

essential tool in modeling the intricacies

of our multifaceted universe.

A 3-D visualization of a pair-instability

supernova, in which nickel-56 decays in

the orange area [17].

ATERUI II, the 1005-node Cray XC50

system for supercomputing at the Center

for Computational Astrophysics at

the NAOJ [16].


Undoubtedly, scientific discovery is at

the essence of humankind, as our curiosity

drives us to better understand and

adapt to the natural and physical world

we live in. In order to access scientific

discovery, we must have the necessary

tools, especially as the questions we ask

are becoming more complex and data is

becoming more ubiquitous. Outer space

continues to feature so many questions

left to answer, yet with profound implications

for humankind. The overarching,

large-scale nature of the physical processes

that govern celestial bodies begs

for further research and analysis to learn

more about unknown parts of the universe.

Yet, we are now better equipped

than ever to tackle these questions. We

can find trends in the seemingly unpredictable

and using logic, algorithms,

and data through computer programs,

creating a toolbox of methods that can

revolutionize astronomy and astrophysics

research. Ultimately, as we strive to

construct a world view of how the universe

functions, we will be able to make

the most of large portions of data from

a variety of research institutions while

fostering collaboration and connected

efforts by citizens, scientists, and governments



[1] Zhang, Y., & Zhao, Y. (2015). Astronomy

in the Big Data Era. Data Science

Journal, 14(0), 11. doi:10.5334/dsj-2015-011

[2] Sumner, T. (2019, June 26). The first

AI universe sim is fast and accurate-and

its creators don’t know how it works.

Retrieved November 25, 2020, from


[3] Armstrong, D. J., Gamper, J., & Damoulas,

T. (2020). Exoplanet Validation

with Machine Learning: 50 new validated

Kepler planets. Monthly Notices

of the Royal Astronomical Society.


[4] S. T. Bryson, M. Abdul-Masih, N.

Batalha, C. Burke, D. Caldwell, K. Colon,

J. Coughlin, G. Esquerdo, M. Haas,

C. Henze, D. Huber, D. Latham, T. Morton,

G. Romine, J. Rowe, S. Thompson,

A. Wolfgang, 2015, The Kepler Certified


SkyShot Autumn 2020

False Positive Table, KSCI-19093-003

[5] Staff, S. (2020, August 25). 50

new planets confirmed in machine

learning first. Retrieved November

25, 2020, from https://phys.org/


[6] Morton, T. D. (2012). AN EFFI-




Journal, 761(1), 6. https://doi.


[7] Shallue, C. J., & Vanderburg, A.

(2018). Identifying Exoplanets with

Deep Learning: A Five-planet Resonant

Chain around Kepler-80 and

an Eighth Planet around Kepler-90.

The Astronomical Journal, 155(2), 94.



[8] 1.7. Gaussian Processes — scikitlearn

0.23.2 documentation. (2020).

Scikit-Learn.Org. https://scikit-learn.


[9] Yeung, J., & Center/NASA, D.

(2020, August 26). Artificial intelligence

identifies 50 new planets

from old NASA data. Retrieved November

25, 2020, from https://news.


[10] Tadaki, K.-, Iye, M., Fukumoto,

H., Hayashi, M., Rusu, C. E., Shimakawa,

R., & Tosaki, T. (2020). Spin

parity of spiral galaxies II: a catalogue

of 80 k spiral galaxies using big data

from the Subaru Hyper Suprime-Cam

survey and deep learning. Monthly

Notices of the Royal Astronomical

Society, 496(4), 4276–4286. https://


[11] Overview of Subaru Telescope:

About the Subaru Telescope: Subaru

Telescope. (n.d.). Retrieved November

25, 2020, from https://subarutelescope.org/en/about/

[12] Kojima, T., Ouchi, M., Rauch,

M., Ono, Y., Nakajima, K., Isobe, Y.,

Fujimoto, S., Harikane, Y., Hashimoto,

T., Hayashi, M., Komiyama, Y., Kusakabe,

H., Kim, J. H., Lee, C.-H., Mukae,

S., Nagao, T., Onodera, M., Shibuya,

T., Sugahara, Y., … Yabe, K. (2020). Extremely

Metal-poor Representatives Explored

by the Subaru Survey (EMPRESS).

I. A Successful Machine-learning Selection

of Metal-poor Galaxies and the Discovery

of a Galaxy with M* < 106 M ⊙

and 0.016 Z ⊙. The Astrophysical Journal,

898(2), 142. https://doi.org/10.3847/1538-


[13] Stephens, T. (2020). Powerful new

AI technique detects and classifies galaxies

in astronomy image data. Retrieved

November 25, 2020, from https://news.


[14] Hosch, W. L. (2019, November 28).

Supercomputer. Retrieved November 25,

2020, from http://www.britannica.com/


[15] HPC Basics Series: What is Supercomputing?

(2019, March 11). Retrieved

November 25, 2020, from http://www.


[16] Peckham, O. (2020, July 24). Supercomputer

Simulations Delve Into

Ultra-Powerful Hypernovae. Retrieved

November 25, 2020, from http://www.


[17] Gough, E. (2020, July 21). Supercomputer

Simulation Shows a Supernova

300 Days After it Explodes. Retrieved

November 25, 2020, from http://www.


[18] C., H. (2020, September 25). Scientists

May Have Developed New

Way to Detect ‘Invisible’ Black Holes.

Retrieved November 25, 2020, from


[19] Penn State. (2020, August 3).

Unequal neutron-star mergers create

unique ‘bang’ in simulations. Science-

Daily. Retrieved November 24, 2020

from www.sciencedaily.com/releases/2020/08/200803184201.htm


SkyShot Autumn 2020

Advancements in Aerospace

Rutvik Marathe

Rocket science: just about one of the easiest subjects in the

world. While we see launches becoming commonplace today,

this wasn’t at all the case just about 100 years ago. That’s right

- the venture of spaceflight is a very new one, requiring the

most precise and powerful technologies we have ever made. It

is far from easy; there are dozens of hurdles to overcome and

situations to account for. The most notable of these challenges

is due to Earth’s gravitational field, as lifting a rocket off the

ground and sustaining its flight needs a lot of fuel. So much so,

that roughly 80-90% of it is just fuel, preventing us from actually

carrying much into space. If that wasn’t enough, the more

fuel you carry, the more additional fuel you have to bring for

that original fuel. However, even with challenging problems

like these, society has made a lot of recent progress in launching

things into space. From companies like SpaceX, Boeing,

and Lockheed Martin, to government organizations like the

Indian and Chinese space agencies, we have been overcoming

the massive challenges that spaceflight presents by launching

every few weeks. So how did we get to this point?

Modern rocketry began to develop in the late 1800s

and early 1900s. The development of aviation led to the first

attempts to launch things off the ground, and using fuel

propulsion came soon after. At this time, all flight was limited

only to the Earth’s atmosphere. But these initial steps helped

establish how rockets function (expelling something downwards

to go up), and the process of enhancing them could

start soon. From that point, early attempts to launch things

into space were made. The first successful space launch came

during the Cold War, when the Soviet Union launched the

satellite Sputnik 1. This was huge news! A country had finally

succeeded in putting something in orbit around the Earth.

And although the R7 rocket that launched it was not very

powerful compared to modern standards, it was a monumental

development in rocketry.

Given the previously mentioned challenges, like the high

fuel requirements for rockets, meant that it wasn’t as easy as

“just making a bigger rocket” to launch heavier things. That

approach would mean that we would need gigantic rockets -

much bigger than the ones we use today - to launch people and

supplies into space. It was clear that we had to make advancements

in the way the rocket was constructed and launched,

rather than simply keep the current system, but make it larger.

Significant advancements came during the time of the space

shuttle, in the 1980s. New materials were being tested for structures

like the fuel tank, most notably an aluminum lithium alloy.

This new material reduced the rocket’s weight by about

20%, making launching and escaping Earth’s gravitational well

easier, and allowing for greater payloads for missions. Another

big advancement in this era was made in reusability. At first,

rockets were designed to be one-time-use only, as recovery was

too complicated a process to attempt early on. NASA’s Space

Shuttle was the first breakthrough in this field, as it captured

and reused the shuttle on many missions. Additionally, SpaceX

has been a pioneer in creating reusable rocket boosters, which

fly back to Earth and land on a platform in the middle of the

ocean. Such technologies make space missions much cheaper

and allow them to run quicker, as you don’t need to invest time

and energy into remaking these parts of the rocket.

Even with the current growth rate of technology, conventional

propulsion (burning fuel like we do today) doesn’t seem

like a long-term option if we want to expand past the Earth.

Such movement beyond our own planet would need a high frequency

of space missions, and therefore a lot of fuel. Not only

is this too costly for any groups to carry out, it is also not a re-


SkyShot Autumn 2020

NASA’s Evolutionary Xenon Thruster Project’s 7-kilowatt

ion thruster. Source: NASA.gov.

Illustration of spacecraft powered by nuclear thermal propulsion.

Source: NASA/Marshall.

newable energy source, so there is only a certain amount

of it available for use [1]. The resource-heavy launch process

right now could be improved through alternative

propulsion methods. These methods, if made efficient,

could be the next big advancement which would allow us

to travel to our solar system and beyond.

One of these is electric propulsion - using electrical

energy to shoot ions out of the rocket and making the

rocket go forward via Newton’s third law. While the tiny

mass of ions means that the thrusters produce very low

acceleration, electricity would not be very hard to gather

and mass produce. In fact, solar panels on a rocket could

even “collect” fuel as the mission progresses! This type of

clean energy for propulsion is being heavily researched

and tested as a major source of fuel.

Another approach being looked at is nuclear propulsion,

where nuclear reactions in a rocket will burn hydrogen

out of the end of the rocket, propelling it forward [2].

This technology is also being developed right now as a

possible “use” of all the nuclear bombs sitting idle in underground

bunkers. Many agree that they would be put

to better use in a rocket engine than being inactive (and

potential apocalypse devices!).

While these technologies are promising for the future,

they don’t seem to be as powerful as the traditional methods

of burning fuel. It is very likely that in the future,

rockets use hybrid varieties of propulsion for different

scenarios. For example, they could use traditional fuels

for initial speed to leave the Earth’s surface, but electric

or nuclear propulsion to navigate through space.

There are many advancements that have been made in

rocketry, and many more to come. Rocketry is catching

speed as many private companies have joined the arena,

competing to make the cheapest and most efficient rockets

possible. For the next few decades, advancements in

this field will only continue to grow, fueled by the uniting

goal of expanding humanity past Earth!


[1] Bruno, T. (n.d.). The Properties of RP-1 and RP-2

MIPR F1SBAA8022G001. https://kinetics.nist.gov/Real-


[2] 6 Things You Should Know About Nuclear Thermal

Propulsion. (2020b, January 21). Energy.Gov. https://



SkyShot Autumn 2020

Access to Space

Carter Moyer

It was almost ten years ago when the Space Shuttle Atlantis

touched down for the last time, bringing a close to the thirty-year-long

program and the United States’ ability to send

humans into space. Ever since, NASA and ESA have relied on

the fourth generation Roscosmos’ tried and true Soyuz rocket—one

that was far cheaper, older, and safer than the American-made

shuttle. The past two Presidential administrations

have pushed for that to change, initiating the commercial crew

program and calling for the United States to once again partake

in human spaceflight, and this time under the veil of neoliberal


The program has largely supported space-industry-veteran

Boeing’s Starliner and astronautical-startup SpaceX’s Crew

Dragon programs, but delay after delay[1], cost overrun after

overrun[2], and an inflight safety test failure[3] has cast a shadow

on the program. And yet, in the middle of a global pandemic

and mass mobilizations against systemic racism and

anti-Blackness, something miraculous happened: SpaceX, not

Boeing, safely ferried two astronauts to and from the International

Space Station. There might have been a little booster

landing thrown in there as well.

This year has been full of space-related surprises and accomplishments,

actually. NASA’s Mars 2020 Rover, now named

Perseverance[4], and paired with its copter buddy, Ingenuity[5],

are on their way to the red planet. This rover is uniquely

poised to continue the goal of searching for life and surveying

Mars—the lightweight aerial drone will be able to go to areas

that Perseverance cannot, either due to geography or the sheer

time it would take to get to them. The rover will also be leaving

behind cached regolith samples[6] in the hopes that future

missions will be able to gather and study them.

And the United States is far from the only active participant

in space this year. China is hoping to launch its Chang’e-5

mission[7] later this year, carrying a lander that will collect

and then return lunar regolith samples to Earth, the first of

its kind since the Soviet Union’s Luna 24 mission. The United

Arab Emirates even had a Mars mission of its own, Hope[8],

launched aboard a Japanese Mitsubishi rocket.

All of these missions are, whether wholly or significantly,

supported by governments with many also focusing on scientific

discovery. There is also, undoubtedly, a large degree of national

pride intertwined with these missions, but a motive that

is relatively absent is profit.

The 2010s have been the launchpad for the 2020s’ space

boom as nation states and multinationals alike pour money

into fleeing a planet literally and metaphorically on fire. Governments

will continue to launch scientific, exploratory, and,

yes, vanity missions, but what we are increasingly seeing is the

private sector taking up the monetization of space. It’s not a

new concept nor is it an under-discussed one, but it is starting

to come to fruition, specifically with SpaceX’s Starlink program[9].

There are currently over five hundred SpaceX satellites

in low Earth orbit, primed to offer internet connectivity

to the public.

With thousands of more satellites planned and the telecommunications

industry handling trillions of dollars every year,

SpaceX is primed to make a lot of money[10] once these satellite

constellations are operational. And with SpaceX also operating

one of the only ways to get these satellites into LEO, the

only other private corporations that could compete with them

would be a pact between Amazon and Blue Origin. Enter Jeff

Bezos’ Kuiper constellation[11].

This seems to be a much more immediate, much more profitable

way to monetize space compared to the oft-lauded space

tourism industry which can only cater to a small number of

high-net-worth individuals and has no room for error[12].

Such an industry shift is also poised to redefine, or at the very

least close the gap between, the roles of governments and corporations

in space. For the longest time, only governments

could fund and take on the risk of space exploration. It’s why

the only consistent customers for sending people to the ISS

are governments. Yet, many people are aware of Elon Musk’s

plan to send people to Mars—it is the main mission of SpaceX,

its prime directive. To do so would be inordinately risky and

costly. Much like how Amazon Web Services is able to subsidize

the rest of Amazon, however, Starlink may very well be

the key ingredient[13] in paving the way for Elon Musk’s billionaire

space fantasies to become reality. The same applies to

Jeff Bezos, Blue Origin, and Amazon.

It’s far from the democratization of space once promised,

but this decade will determine whether the keys to space remain

exclusively in the hands of governments or are shared

with the megarich.


[1] Wattles, J. (2019, November 16). Boeing and SpaceX face

‘significant’ challenges in delayed NASA program. https://



[2] Smith-Schoenwalder, C. (2019, June 20). GAO: NASA

Programs Rack Up Delays, Cost Overruns. U.S. News & World

Report. https://www.usnews.com/news/national-news/articles/2019-06-20/gao-nasa-programs-rack-up-delays-costoverruns.

[3] Sheetz, M. (2020, March 6). NASA investigation finds

61 corrective actions for Boeing after failed Starliner spacecraft

mission. CNBC. https://www.cnbc.com/2020/03/06/


SkyShot Autumn 2020


[4] Potter, S. (2020, July 30). NASA, ULA Launch Mars

2020 Perseverance Rover Mission to Red Planet. NASA.


[5] Northon, K. (2018, May 11). Mars Helicopter to Fly

on NASA’s Next Red Planet Rover Mission. NASA. https://


[6] Johnson, A., & Hautaluoma, G. (Eds.). (2020, June

17). The Extraordinary Sample-Gathering System of NA-

SA’s Perseverance Mars Rover – NASA’s Mars Exploration

Program. NASA. https://mars.nasa.gov/news/8682/the-extraordinary-sample-gathering-system-of-nasas-perseverance-mars-rover/.

[7] Jones, A. (2020, August 6). On its way to Mars, Chinese

spacecraft spots Earth and moon, aces steering maneuver.

Space.com. https://www.space.com/china-marsmission-spots-earth-and-moon.html.

[8] Bartels, M. (2020, July 19). United Arab Emirates

launches ‘Hope’ mission to Mars on Japanese rocket. Space.

com. https://www.space.com/hope-mars-mission-uaelaunch.html.

[9] Etherington, D. (2020, July 15). Leak reveals details

of SpaceX’s Starlink internet service beta program. Tech-

Crunch. https://techcrunch.com/2020/07/15/leak-revealsdetails-of-spacexs-starlink-internet-service-beta-program/.

[10] Sheetz, M. (2020, July 20). Morgan Stanley: SpaceX

could be a $175 billion company if Elon Musk’s Starlink

internet plan works. CNBC. https://www.cnbc.



[11] Grush, L. (2020, July 30). FCC approves Amazon’s internet-from-space

Kuiper constellation of 3,236 satellites. The

Verge. https://www.theverge.com/2020/7/30/21348768/


[12] Australian Associated Press. (2018, December 17).

Richard Branson’s Virgin Galactic space flights criticised

as ‘dangerous, dead-end tech’. The Guardian. https://


[13] Sheetz, M. (2019, May 16). Elon Musk says SpaceX Starlink

internet satellites are key to funding his Mars vision.

CNBC. https://www.cnbc.com/2019/05/15/musk-on-starlink-internet-satellites-spacex-has-sufficient-capital.html.

Falcon 9 lifting off from the historic Launch Complex

39A, sending Crew Dragon to orbit on May 30, 2020. Source:



SkyShot Autumn 2020

Figure 1: The Timeline of the Universe (NASA, 2006)

Understanding the Chronology of

the Universe, from the Big Bang

to the End of Time

Andrew Tran


Understanding the past and future of our universe is an idea

that cosmologists have worked on for several decades, tying

into several big-picture, philosophical questions, such as “Why

are we here?” or “What is the destiny of humanity in this vast

universe?” Using theoretical models, calculations, and observations,

physicists have been able to determine the stages and

conditions that the universe has experienced from the Big Bang

to today. From what astronomers have measured, it has also

been possible to predict how the universe will look hundreds

of billions of years into the future. After analyzing the densities

of baryonic matter and dark energy, it has become known that

the universe is expanding at an accelerated rate, and using this

information allows for calculated inferences about the behavior

of the universe throughout its chronology, going as far back

as 13.7 billion years ago.

The Moment of Creation

The first stage in the timeline goes back to about 13.7 billion

years ago, where it all began with the Big Bang. This moment

is often referred to as the ‘Planck epoch’ or the ‘Grand unification

epoch’, and marks a period of time that wasn’t even a

microsecond long [4]. When the universe was at this stage, all

of the four fundamental forces of nature, that being the three

in the Standard Model (strong nuclear, weak nuclear, and electromagnetic),

and gravity, were bonded together. The universe

was extremely high in temperature, at around 1030 degrees


A common misconception with the Big Bang is that it was

an explosion that allowed the universe to exist à la Genesis,

when really it was more like all of space expanding violently at

once, increasing the distance between all of the structures in

the universe that would eventually become galaxies and stars.

The Big Bang truly marks the transition that the universe took

from being barely a few millimeters across, to the cosmic size

that we can see today [2]. It is often denoted as the ‘birth’ of our

universe because it’s where the fundamental ideas and laws of

physics that we know today, such as general relativity and quantum

mechanics, begin to work. This is where four fundamental

forces of physics, that being the gravitational, strong nuclear,

weak nuclear, and electromagnetic force began to break down,

and separate. We have been able to validate and justify the Big

Bang Theory, as it provides an explanation for many observations

we’ve made, such as the Cosmic Microwave Background

(CMB) and Hubble’s Law which indicates the expansion of the



SkyShot Autumn 2020

The Infant Universe

Next is the period of time when the universe was only a few

hundred thousand years old, just an infant compared to its age

today. At this point, the scale of the cosmos had already begun

to inflate. The tiny subatomic fluctuations within the fabric of

the universe at this stage are speculated to have been the seeds

for what would someday become galaxies.

Figure 2: Galaxies like

NGC 4414 formed

thanks to tiny quantum

fluctuations. (NASA/

Hubble, 1999)

During infancy, the universe began to form several kinds

of subatomic particles, which would someday be classified as

quarks, leptons, and gauge bosons [6]. From these subatomic

particles, a large amount of matter and antimatter were

formed, which annihilated one another whenever they interacted.

However, the amount of matter just slightly exceeded

the amount of antimatter, so that’s why today there’s only

mostly matter in the universe today (though, if it was antimatter

that was more abundant, we would have just ended up calling

that matter anyways).

About 1 second after the Big Bang, protons and neutrons

(the essential building blocks of atoms) formed, and at around

2 minutes, collided, creating heavier elements such as deuterium

[7]. For about 50,000 years, the universe was too high

in temperature for light to be able to travel, so it was just a

cloudy, blurry plasma permeating everywhere. Eventually, the

universe began to cool down, and began to be dictated by matter

instead of radiation, forming the first molecules [8].

them. The atoms no longer scatter light, so they can now travel

freely, illuminating the stage of the cosmos. Atoms that were

recently formed release photons that can still be detected today

in the cosmic microwave background radiation, which is the

furthest we can peer back in time into the cosmos—glimpses

of the leftover radiation emitted during this era, at the microwave


The Dark Ages

Unlike the Dark Ages following the fall of the Roman Empire,

the Dark Ages refer to a time in the universe, lasting nearly

a billion years, when the first stars and galaxies in the universe

had yet to shine. The cosmos were making the transition

from out of the “soup” of subatomic particles.

What made the universe so “dark” at this time was that the

light that could now travel freely was affected by the expansion

of the universe, stretching out or red-shifting into wavelengths

of light not in the visible spectrum. This darkness would end

up lasting hundreds of millions of years. During the Dark Ages,

the majority of the matter that occupied the universe included

dark matter, and neutral, uncharged amounts of hydrogen and

helium [3].

Figure 4:

An artistic


of dark matter


Eventually, the most ancient stars and galaxies began to

form, due to the accumulation of baryonic (ordinary) matter

and dark matter into disk-like structures. This point is commonly

referred to as the “Epoch of Reionization” [3]. Galaxy

clusters would begin to form, slowly transitioning the universe

out of the cosmic dark ages.

The Present Day (Galaxy Era)

Figure 3: The Cosmic Microwave Background Radiation

(NASA/WMAP, 2010)

Over 300,000 years later, with temperatures much lower

now, neutral atoms could be produced. This is the epoch

known as “recombination.” Ionized atoms were formed as

well, including hydrogen and helium, which are still the most

abundant elements in the universe today. As we reach the end

of the universe in its infancy, it starts to become transparent,

since the ionized atoms have attracted electrons, neutralizing

After the dark ages, we’re brought to the present day, often

referred to as the ‘galaxy era’ of the universe. Sometime into

this stage, the Milky Way, then our solar system, and then the

Earth entered the universe. And then just under 13.7 billion

years following the Big Bang, the human race walked the Earth

for the first time. If you were to scale down the entire history

of the universe from the Big Bang until today into one calendar

year, humans would have appeared just before midnight

on New Year’s Eve.

Figure 5: The

golden age of

our universe?




SkyShot Autumn 2020

There is an estimated maximum of two trillion galaxies in

the observable universe. Given our observations of the incoming

light from galaxies, we have been able to conclude that the

universe is expanding at an accelerating rate. The more matter

and mass there is in an object, the more gravitationally attractive

it will be, so one would expect that the combined masses

of all the galaxies and groups of galaxies in the universe would

result in everything collapsing in on one another. Since this

isn’t the case, it means there is a mysterious force, which we

still don’t know much about, pushing everything apart: dark

energy. We have been able to conclude that dark energy makes

up 68% of everything in the universe, dark matter makes up

27%, and normal, baryonic matter to be barely 5% [5]. This

makes sense since the universe can only accelerate if the density

of matter is less than the density of dark energy.

If the universe is expanding at an accelerating rate, that

would mean that the galaxies are getting further apart from

one another. Eventually, humanity will see fewer and fewer

stars in the night sky. Our descendants, several thousands of

years into the future, may not get to enjoy astronomy and stargazing

as we get to today.

Eventually, the last stars in the universe will dim, maybe explode

in a supernova, but then eventually shut off for eternity.

This brings us to the last stage in our timeline of the universe.

What will be in store for existence as we know it?

The Future and Fate of Our Universe

The last stage of the universe timeline brings us to a point

where the stars and galaxies begin to stop forming. The universe

continues to expand at an accelerating rate, due to the

effects of dark energy. Given current models and data that we

have in cosmology, the most likely scenario that the universe

will experience is the “Big Freeze,” in which the universe will

keep expanding until it reaches a temperature of absolute zero.

Some other theories, such as the “Big Rip” and the “Big Crunch”

involve the universe going out in a spectacular and flashy way.

But the one that seems to be our destiny is cold and silent.

Eventually, once all of the stars have lived out their lives, all

that will be left in the universe are black holes, constantly feeding

on anything that gets near them, and maybe a few white

dwarfs [1]. It has been theorized that by this point protons will

decay as well. The beautiful cosmos that we once knew will become

a bizarre place mostly occupied by stellar corpses twisting

and turning spacetime. During this period, black holes may

merge together and release gravitational waves.

However, all things in the universe must come to an end,

and this includes black holes. Due to the phenomenon known

as Hawking radiation, black holes over time will evaporate as

a result of the quantum effects near the event horizon, the

boundary at the edge of the black hole, or “point of no return”

where nothing may escape [1]. Once the last black hole dies,

the universe will see a glimmer of light one last time, when the

last stellar remnant evaporates. Then, everything will go dark.

Life will be unable to thrive in this universe anymore. The concept

of time will become irrelevant.

Perhaps it won’t be all bad, though. The last survivors, which

may include humans, could find a way to escape this universe,

and go to an entirely different one. Physicists have for many

years postulated the idea of a multiverse, and if it’s true, then

life—humanity, could live on to see another day.


[1] Adams, Fred C.; Laughlin, Gregory (1997). “A dying universe:

the long-term fate and evolution of astrophysical objects”.

Reviews of Modern Physics. 69 (2): 337–372. arXiv:astro-ph/9701131.

Bibcode:1997RvMP...69..337A. doi:10.1103/

RevModPhys.69.337. S2CID 12173790.

[2] Bridge, Mark (Director) (30 July 2014). First Second of the

Big Bang. How The Universe Works. Silver Spring, MD. Science


[3] Byrd, D. (2017, July 16). Peering toward the Cosmic Dark

Ages. EarthSky. https://earthsky.org/space/cosmic-dark-ages-lyman-alpha-galaxies-lager

[4] Chow, Tai L. (2008). Gravity, Black Holes, and the Very

Early Universe: An Introduction to General Relativity and Cosmology.

New York: Springer. ISBN 978-0-387-73629-7. LCCN

2007936678. OCLC 798281050.

[5] Dark Energy, Dark Matter. (n.d.). Retrieved November 26,

2020, from https://science.nasa.gov/astrophysics/focus-areas/


[6] First Light & Reionization - Webb/NASA. (n.d.). Retrieved

November 26, 2020, from https://jwst.nasa.gov/content/science/firstLight.html

[7] Kolb, Edward; Turner, Michael, eds. (1988). The Early

Universe. Frontiers in Physics. 70. Redwood City, CA: Addison-Wesley.

ISBN 978-0-201-11604-5. LCCN 87037440. OCLC


[8] Ryden, Barbara Sue (2003). Introduction to Cosmology.

San Francisco: Addison-Wesley. ISBN 978-0-8053-8912-8.

LCCN 2002013176. OCLC 1087978842.

[9] WMAP Big Bang Theory. (n.d.). Retrieved November 26,

2020, from https://map.gsfc.nasa.gov/universe/bb_theory.



Figure 6: The universe will be dominated by these

stellar predators. (NASA/JPL, 2013)

SkyShot Autumn 2020

2002 KM6 (99795)

Naunet Leonhardes-Barboza

single points of excited light

sparkle the darkness

a chill breeze in the latest hours of the night

—blink, there she is flying

magnitudes brighter than her neighboring stars

she still stands out

a white dot among a sea of white dots

method of gauss

Naunet Leonhardes-Barboza

switch into gaussian time

we stare at the whiteboard, exhausted

perhaps, similar to carl once did

he looked at his desk

contemplating questions for the universe

there are no limits, and he wants to find ceres

ask your fellow intellect, like he did kepler

laugh and cry when it’s over

we now know where that small rock is going


SkyShot Autumn 2020

tessellated constellations

Alex Dong

gazing upon the expanses

of the great night sky

our endless wonder

at the mosaic sea of stars

is inexplicably bridled

by an intangible feeling that

the abyss separating

our world and distant realms

forms a chasm of black emptiness

meant to isolate different beings

are we imprisoned on this earth

destined to stay indefinitely

watching other worlds pass by

time and time again until

time can no longer describe

our passivity to their passing

through a window of obscurity

permitting only imagination to

penetrate its dark tinted display

of anonymous secrecy

and us the victims of our own nature

able to imagine but not to reach out

and satisfy our insatiable curiosity

we begin to realize the paradoxical

plight of human existence

as our seemingly powerful earthly

dwelling is humbled by our

temporary and insignificant

presence in the vastness of

the galaxy and the cosmos


so we are fated to be

mighty yet powerless

an existence woven in

a tessellated fabric of

captivating constellations and

starry specks mirroring

the very state of our planet

our world our reality

brimming with wonder and

glimmering with hope

SkyShot Autumn 2020

starry dreams

Alex Dong


dance around in twilight

a few points on Nyx’s blanket

incomprehensible yet

sought to be understood

but neither wonder nor courage

can capture the essence of

their immortal shine


their luminescence a perpetual comfort

their presence an eternal gaze yet

their longevity a reminder that

our time spent curiously probing is

just a second in their eon

their time is not ours and

still we look on


on the edge of the galaxy

have already lived their lives

and faded into oblivion

our witness of their glory

an elegy to the past

time itself a warped cycle

of dreamy slumber and starry imagination


SkyShot Autumn 2020

images of the past

Naunet Leonhardes-Barboza

she can feed her own intellectual curiosity

from the fruit of the delectable tree of life

a red apple will fall upon her own head

so she, too, can discover something

she looks up at the night sky in awe

unaffected by her immediate environment

she, much like these glimmering dots

is not yet disillusioned

by the harsh realities of humanity

but there was a time she almost gave in,

she believed the stars were static

they would never attempt to deceive her

unlike the unforeseen friend or foe

the illusion of simplistic dots

the tempting twinkling as if saying “look at me closer”

yet, they were clouded by natural gases in Earth’s air

the stars can’t show her everything at once

unless with aid for her human eyes

she learned the truth about the lives of the stars

she took images of the past millions of years ago

she observed rainbows of color in the black void

and the intensity of rocks flying through space

she wondered if the stars would look back at her

and dismiss her life as a dot, dim with little hope

perhaps the stars and her feel the same way?

wishing to project the light of the present

and show each other their grand, dynamic journeys


SkyShot Autumn 2020

unseen skies

Alex Dong

they say

shoot for the moon

even if you miss

you’ll hit the stars

but some shots fall short

and the stars they never

witness our dreams

answer our prayers

our shots ring unheard

through the infinite darkness

the unknown chasm


yet we shoot and we work

almost mindless in repetition

almost mechanical in movement

almost purposeless in routine

we become an echo

perpetuating the mechanism

that bounds us to its cycle

stifled in our own gasps for breath

our spirits dim and flicker

as gatsby’s green light winks out

our dreams can only remain

faraway fantasies forever

but through pensive nights we gaze up

to wisps of black and rays of dark

in search of the place the purpose

of our endless tunnel of toil

these dark skies yield no answer

yet somewhere within us we know

that honest truth lies far beyond

horizons we may perceive

so when darkness falls at the thirteenth hour

we shoot again hoping

one day


we’ll reach the unseen skies


SkyShot Autumn 2020

Astrophotography Details

Ryan Caputo - Tulip Nebula (Sh2-101)

Dates: July 4, 2020, July 5, 2020, July 8, 2020

Imaging and Guiding:

Guan Sheng Optical Classical Cassegrain 6” F/12

Mount: iOptron CEM60

Imaging camera: ZWO ASI1600MM.

Guiding camera: ZWO ASI 290mm Mini

Editing Programs and Techniques:

Software: N.I.N.A , PixInsight 1.8 Ripely.

Filters: Radian Triad-Ultra

Ryan Caputo - Hercules Galaxy Cluster (Abell 2151)

Dates: April 28, 2020, May 18, 2020, May 29, 2020, May 30, 2020

Imaging and Guiding:

Guan Sheng Optical Classical Cassegrain 6” F/12

Mount: iOptron CEM60

Imaging camera: ZWO ASI1600MM.

Guiding camera: ZWO ASI 290mm Mini

Editing Programs and Techniques:

Software: N.I.N.A , PixInsight 1.8 Ripely.

Wilson Zheng - Horsehead Nebula (Barnard 33)

Date: March 27, 2020

Location: Dixon Observatory, Berkshire School, MA, USA

Equipment: Mead LX200 GPS 14” f/10

Camera Details: ZWO ASI1600MC

Acquisition Details: 27 @ 60 seconds

Editing Programs and Techniques:

Processed with PIXINSIGHT

Wilson Zheng - Messier 3 (near Bootes)

Date: April 16, 2020

Location: Dixon Observatory, Berkshire School, MA, USA

Equipment: Mead LX200 GPS 14” f/10

Camera Details: ZWO ASI1600MC

Acquisition Details: 240 @ 30 seconds

Editing Programs and Techniques:

Processed with PIXINSIGHT

Wilson Zheng - Sunflower Galaxy (Messier 63)

Date: March 27, 2020

Location: Dixon Observatory, Berkshire School, MA, USA

Equipment: Mead LX200 GPS 14” f/10

Camera Details: ZWO ASI1600MC

Acquisition Details: 360 @ 30 seconds

Editing Programs and Techniques:

Processed with PIXINSIGHT

Anavi Uppal - Star Trails over Pierson College

Date: August 26, 2020

Location: Pierson College at Yale University

Camera: Nikon D500

Lens: Rokinon 10mm F2.8 Ultra Wide Angle Lens

Sky: 84 images @ f/2.8, 10mm, ISO 1600, 30 sec.

Foreground: 1 image @ f/2.8, 10mm, ISO 1600, 1.6 sec

Anavi Uppal - Comet NEOWISE (Wide)

Date: July 19, 2020

Location: Orlando, Florida

Camera: Nikon D500.

Lens: Nikon AF-P DX NIKKOR 18-55mm f/3.5-5.6G VR

Acquisition Details: 12 images @ f/5.6, 55mm,

ISO 1600, 6 sec

Anavi Uppal - Comet NEOWISE (Zoomed)

Date: July 19, 2020

Location: Orlando, Florida

Camera: Nikon D500.

Lens: Nikon AF-P DX NIKKOR 18-55mm f/3.5-5.6G VR

Acquisition Details: 12 images @ f/5.6, 22mm,

ISO 1600, 8 sec

Owen Mitchell - Comet NEOWISE

Date: mid-July 2020

Location: Bozeman, Montana

Equipment: William Optics Redcat 51

Camera Details: Canon SL2 at 250mm on an i

Optron SkyGuider Pro

Acquisition Details: Stacked exposures worth 2 minutes

Owen Mitchell - Milky Way Galaxy

Date: Summer 2019

Location: Etscorn Campus Observatory at

New Mexico Tech

Camera Details: Canon SL2 with a tiki on 14mm lens on

an iOptron SkyGuider Pro

Acquisition Details: 25 seconds

Nathan Sunbury - The Ring Nebula (Messier 57)

Date: Summer 2016

Location: Sommers-Bausch Oservatory,

University of Colorado Boulder


Nathan Sunbury - The Moon

Date: Summer 2016

Location: Sommers-Bausch Observatory,

University of Colorado Boulder

SkyShot Autumn 2020

Cameron Woo - Pleiades (Messier 45)

Date: January 20, 2020

Location: New Jersey

Camera Details: AF-S Nikkor 55-300mm f/4.5-5.6G

ED DX lens at 300mm, f/5.6, and 1 second

Acquisition Details: 160 light frames at ISO 6400,

18 darks, 22 bias, and 18 flats

Software: stacking performed in SiriL in MacOS,

editing performed in Affinity Photo

Cameron Woo - The Summer Triangle Asterism

Date: August 11, 2020

Location: New Jersey

Camera and Acquisition Details:

Rokinon 16mm f/2.0 lens for crop sensor

Nikon cameras at f/4.0, 25 seconds, ISO 3200

Additional Details: 130 light frames, 20 darks,

20 bias, and 18 flats

Software: stacking performed in Deep Sky Stacker on

Windows 10, editing performed in Affinity Photo

Cameron Woo - Milky Way Galaxy

Date: August 30, 2016

Location: Kaanapali in Maui in bortle 3-4 skies

Camera Details: Nikon AF-S Nikkor 35mm f/1.8G DX lens

Acquisition Details: f/1.8, at ISO 800 or 1600

for 5-8 seconds

Software: frames stacked in Deep Sky Stacker on

Windows 10, edited in Affinity Photo

Jonah Rolfness - Sadr Region

Mount: Ioptron IEQ30 Pro

Camera: ASI1600mm

Filters: Astronomik 6nm Ha, OIII

Optics: Rokinon 85mm F1.4 lens

Acquisition Details: 600x120sec Ha shot at F1.4

234x120sec OIII shot at F1.4


WBPP Script used to calibrate all lights with

darks, flats, and flat darks

SubframeSelector used to apply various weights to each frame

StarAlignment used to register each channel

ImageIntegration used to create masters for each channel,

ESD Rejection Algorithm


DynamicCrop and alignment of each master channel

DBE to remove gradients

Linear Fit channels

MLT Noise Reduction on each channel

Ha very aggressively stretched by STF to increase contrast

OIII given normal STF stretch

Pixelmath used to combine to a HOO palette

Various curve transformations on saturation and

RGB brightness

LHE and MLT Bias applied for sharpening

Aggressive star reduction applied using starnet and

morphological transformation

Clonestamp tool used to salvage crescent and tulip nebulas

after starnet ate them

DarkStructureEnhance Script

Cameron Woo - The Orion Nebula (Messier 42)

Date: late December 2019

Location: Bergen County suburbs 30 minutes from

Manhattan in bortle 8 skies

Camera Details: AF-S Nikkor 55-300mm f/4.5-5.6G

ED DX lens at 300mm, f/5.6, 1 or 1.3 seconds

Acquisition Details: 183 frames at ISO 3200, 70 frames at

ISO 6400, 100 at ISO 12800

Additional Details: 14 flat frames, 36 dark frames,

42 bias frames

Software: stacking performed in SiriL in MacOS,

editing performed in Affinity Photo


SkyShot Autumn 2020


Jonah Rolfness - The Orion Nebula (Messier 42) and

Running Man Nebula (Sh2-279)

Mount: Ioptron IEQ30 Pro

Camera: ASI1600MMC

Filters: ZWO LRBG, Astronomik Ha, SII, OIII

Telescope: GSO 6in F/5 Newtonian Reflector

Autoguider: QHY5L-II-M paired with the

Orion 60mm guide scope

Acquisition Details:

Panel 1:

-40x5, 41x60, 38x300 Ha

-52x120 R

-50x120 G

-48x120 B

Panel 2:

-38x5, 41x60, 42x300 Ha

-51x120 R

-50x120 G

-49x120 B


Kappa Sigma Stacking in DSS to deal with geo sats

HDR combine on Ha stacks for HDR Luminance

MMT Noise Reduction

Histogram stretch

-HDR Transformation to bring the core to proper levels

-Color Combination

-Curve Transformations to bring out red colors,

lower background RBG, and saturation

-Luminance addition

-ACNDR in post-linear state to further reduce noise

-SCNR to reduce green hue

-Star Reduction

Jonah Rolfness - The Pinwheel Galaxy (Messier 101)

Mount: Ioptron IEQ30 Pro

Camera: ASI1600MMC

Filters: Ha/SII/OIII Astronomik 6nm and ZWO LRGB

Telescope: GSO 6in F/5 Newtonian Reflector

Autoguider: QHY5L-II-M paired with the

Orion 60mm guide scope

Acquisition Details:

Roughly 800x120sec Luminance

200x120sec RGB

450x120sec Ha


Stacked and calibrated using DSS Kappa Sigma Clipping


DynamicCrop and alignment of each master channel

DBE to remove gradients

Deconvolution script applied to bring out detail in galaxy core

MLT Noise Reduction on each channel

LRGB Combination to create final master with STF stretch

Separate STF for Ha channel

Various curve transformations on RGB brightness

and saturation

Added in Ha layer using pixelmath to

brighten the red nebula in the galaxy

Jonah Rolfness - The Heart Nebula (IC 1805) - Fish Head

Nebula (IC 1795) and Melotte 15 Mosaic

Mount: Ioptron IEQ30 Pro

Camera: ASI1600MMC

Filters: Ha/SII/OIII Astronomik 6nm

Telescope: GSO 6in F/5 Newtonian Reflector

Autoguider: QHY5L-II-M paired with the

Orion 60mm guide scope

Acquisition Details:

Panel 1: 300x12, 1800x3 Ha; 300x11 OIII; 300x10 SII

Panel 2: 300x21, 1800x4 Ha; 300x24 OIII; 300x20 SII

Panel 3: 300x22, 1800x4 Ha; 300x22 OIII; 300x16 SII

Panel 4: 300x23, 1800x4 Ha; 300x24 OIII; 300x25 SII

Processing- Stacking:

Used DSS to create master lights for short Ha, long Ha, OIII,

and SII

Stacked both long and short Ha to create a master Ha light.

Appropriate darks and flats were used


DynamicCrop and DBE on all 12 stacks(Ha, OIII, SII)

StarAlignment used to create a rough mosaic for each filter

GradientMergeMosiac and DNALinearFit used to create a

final mosaic for each filter.

Noise reduction using MultiscaleLinearTransform applied for

each master frame.

ChannelCombination used to create a master SHO image.

SCNR green applied for both regular and inverted(magenta

star reduction)

Many different curve transformations, boosting saturation,

shifting hue, and reducing RBG background levels.

TGVDenoise and ACNDR applied to further eliminate noise

SkyShot Autumn 2020

Educational Opportunities


The Science Ambassador Scholarship: A full–tuition scholarship

for a woman in science, technology, engineering, or math. Funded

by Cards Against Humanity. Open to high school and undergraduate

students. Applications close December 14th, 2020 at 11:59PM CST.

Richard Holland Memorial Scholarship: Open to high school and

undergraduate students. Applications will be accepted online starting

January 1, 2021 through March 15, 2021.

The Gladys Carol Scholarship Program, Inc: Open to high school

seniors, high school graduates, and current undergraduate level students

who are United States citizens or permanent residents. Application

process opens on January 1, 2021.

SBB Research Group STEM Scholarship: Available to currently

enrolled full-time students pursuing a STEM degree. Awarded on a

quarterly basis in 2021; the next application deadline is February 28,


CC Bank’s Young Scholars Scholarship: Each year CC Bank’s

Young Scholars Scholarship offers up to five $2,000 scholarships to

students attending universities, colleges, and other academic institutions

across the U.S. Applicants must apply by Thursday, December

31, 2020, to get the scholarship during the 2021-2022 academic year.

Lockheed Martin STEM Scholarship: For high school seniors and

current college freshmen or sophomores.

Women in Aerospace Scholarship

Women in Technology Scholarship (WITS) Program

Google Scholarships for Computer Science

Annual Collabera STEM Scholarship

Regeneron Science Talent Search

The Gates Scholarship

Programs and Internships

Summer Science Program in Astrophysics: Open to rising juniors

and seniors in high school. Applications open on December 15, 2020.

California State Summer School for Mathematics and Science

(COSMOS): Open to students in grades 8-12. Applications due in early


NASA SEES: Open to high school students. Applications due in

early 2021.

Yale Summer Program in Astrophysics (YSPA): Open to rising high

school seniors. On temporary hiatus for 2021, will reopen in 2022.

Jet Propulsion Laboratory Summer Internship Program: Open

to undergraduate and graduate students pursuing degrees in STEM

fields. Applications due on March 31, 2021.

Boston University Research in Science & Engineering Program

(BU RISE): Open to rising high school seniors. Applications open on

December 15, 2020.

Research Science Institute by the Center for Excellence in Education

(RSI): Open to rising high school seniors. Applications due on

January 16, 2021.

Science Internship Program at UC Santa Cruz

Research Mentorship Program at UC Santa Barbara

Kopernik Observatory & Science Center High School Internship


Alfred University Astronomy High School Institute

Space Telescope Science Institute Space Astronomy Summer Program

APL Johns Hopkins STEM Academy

International Opportunities

Work Experience at the Australian National University: The observatory offers a limited number of work experience places to

year 10, 11 and 12 students each year. These placements are typically 1 week in duration and the students work on an astronomical

project under the supervision of professional astronomers.

International Astronomy Summer Internship Program: The summer internship is designed for pupils in the final years of high

school, or those who have just finished high school. During their three-week stay, participants work on a variety of astrophysical

observations and experiments.


SkyShot Autumn 2020

Contributor Biographies

Priti Rangnekar is a freshman at Stanford University,

majoring in computer science and engineering physics.

She has researched asteroid orbit determination at

the 2019 Summer Science Program, conducted a datadriven

astrophysics Senior Project at BASIS Independent

Silicon Valley, and analyzes exoplanet transits through

a collaborative project with AAVSO observers. As a

7-time national qualifier and Tournament of Champions

quarterfinalist in speech and debate, as well as a scientific

writer for student journals, she champions the value of

logical thinking and effective communication in a variety

of fields. She has received recognition in international

competitions for computing and business, and she enjoys

conducting STEM outreach. She seeks to connect fellow

students around the world while fostering knowledge

and hands-on exploration of the universe in an inclusive,

engaging community.

Rutvik Marathe is a freshman at the University

of Michigan, majoring in aerospace engineering. An

ardent space enthusiast, Rutvik has conducted asteroid

orbit determination research at the Summer Science

Program in 2019, pursues astrophotography, and has

independently studied topics in orbital mechanics and

chaos theory. In addition to space-related endeavors, he

has earned recognition at the state level in varsity debate

and competitive mathematics, as well as at the national

level in programming. With expertise in team leadership

and teaching STEM, he strives to promote curiosity and

interest for the universe and space exploration through


Naunet Leonhardes-Barboza is a young Costa Rican-

American woman planning to major in astrophysics and

computer science at Wellesley College. She has experience

volunteering for The Rockland Astronomy Club and for

Girls’ World Expo as a Birght Ideas/Science Coordinator.

An alum of the 2019 Summer Science Program in

Astrophysics, she has learned and researched in both the

Stull Observatory and Sommers-Bausch Observatory. She

loves spending my free time writing poetry about space

and further exploring her interest in astronomy through

all mediums.

Carter Moyer is a freshman at Harvey Mudd College,

majoring in engineering and political science. He is also an

alum of the 2019 Summer Science Program in Astrophysics.

Vighnesh Nagpal is a freshman at UC Berkeley hoping

to pursue a double major in Physics and Astrophysics.

He is fascinated by everything ‘space’, having gained

experience doing research as part of the Summer Science

Program in Astrophysics and his experiences working with

scientists at Caltech on exoplanet research. He hopes to

continue exploring and learning about the exciting state

of astronomy today.

Ezgi Zeren is a freshman at Tufts University, majoring in

mechanical engineering. She hails from Istanbul, Turkey.

Although it is difficult to see the stars and all other celestial

objects from Istanbul’s crowded streets, she considers

herself lucky to participate in SSP at CU Boulder in 2019.

“At the Sommers-Bausch Observatory, I looked through a

telescope first time in my life. Since then, I try to share

what I explore looking at the sky with others, who want to

see and know more.”

Anavi Uppal is a freshman at Yale University who plans

on double majoring in astrophysics and computer science.

She is an alum of the 2019 Yale Summer Program in

Astrophysics, where she researched the newly discovered

supernova SN 2019hyk. She was an intern at the 2018

NASA STEM Enhancement in Earth Science (SEES)

Program, where she helped design a lunar habitat and lunar

mission. Anavi greatly enjoys participating in astronomy

outreach, and hopes to inspire others to see the beauty in

our universe. She often volunteers at astronomy nights

and occasionally gives talks to the public. Anavi has been

doing astrophotography for five years, and specializes in

landscape astrophotography. Her work can be viewed on

Instagram at @anaviuppal.

Victoria Lu is a freshman at Yale University. She is

prospectively double majoring in art and evolutionary

biology. She is an alum of the 2019 Summer Science

Program in Astrophysics. Victoria seeks to connect global

communities on contemporary issues, such as climate

change and conservation, through scientific research and



SkyShot Autumn 2020

Alexandra Masegian is a second-year student at

the University of Chicago, where she is majoring in

astrophysics and minoring in data science and creative

writing. Her primary interests lie in stellar and extragalactic

astrophysics, and she has been an astrophysics research

intern at the University of California, Santa Cruz, the

American Museum of Natural History, and Fermi National

Accelerator Laboratory. She is also an alum of the 2018

NASA STEM Enhancement in Earth Science (SEES)

Program, where she was a member of the Explore the Moon

team. Alex is passionate about science communication

and outreach, and hopes to spend her career broadening

humanity’s knowledge of the vast and beautiful universe

we live in. Her work can be found in SkyShot itself, as

well as in The Spectrum, a UChicago-based e-publication

about science and society.

Andrew Tran is a second-year student at the University

of Georgia majoring in astrophysics and minoring in

mathematics. He has been involved in many facets of the

astrophysics community, as a former NASA intern, as an

undergraduate researcher in the Department of Physics

and Astronomy at his school, and as the creator and founder

of Astrophysics Feed, a science media page on Instagram

(@astrophysicsfeed). In his spare time, Andrew likes

astrophotography, reading books about space or physics,

and learning anything about the world and universe.

Sofia Fausone is from Northern California. She is

currently a first year at Yale and hopes to double major in

physics and math. She’s especially interested in theoretical

physics and is excited to explore different areas of each


Timothy Hein is a Computer Engineering Major at

Purdue University. Though his passions include technical

design, coffee, and classical literature, he plans on pursuing

a career in early stage venture capital.

Abby Kinney is a freshman at Williams College interested

in studying physics. Originally from Washington, she is an

alum of the 2019 Summer Science Program in Astrophysics.

In her free time, she enjoys observing the night sky.

Owen Mitchell is a college freshman at Johns Hopkins

University. Originally from Montana, he is an alum of the

2019 Summer Science Program in Astrophysics at New

Mexico Tech.

Jonah Rolfness is a college freshman at the California

Institute of Technology. Originally from Arizona, he is an

alum of the 2019 Summer Science Program in Astrophysics

at New Mexico Tech.

Feli is a high school senior from Ohio interested in

astronomy and astrophysics. They are an alumnus of the

2020 Summer Science Program in Astrophysics, where

they tracked the path of a near-Earth asteroid. In their free

time, they enjoy reading, stargazing, and learning more

about space!

Ryan Caputo is a freshman at the University of Colorado

Boulder. Originally from Texas, he is an alum of the 2019

Summer Science Program in Astrophysics.

Alex Dong is a first-year student at Yale from Canada,

having recently graduated with a bilingual high school

diploma and the AP International Diploma accolade. He

is also an alum of the 2019 Summer Science Program in

Biochemistry. At Yale, he is planning to major in Molecular

Biophysics and Biochemistry, although his passion for

poetry—especially themed around astronomy—will

continue to occupy his spare time.

Nathan Sunbury is a senior at Harvey Mudd College

hailing from California. He is an alum of the 2016 Summer

Science Program at the University of Colorado Boulder.

Sydney Wang is from Dalian, China and went to

the Webb Schools. He currently studies physics at The

University of Pennsylvania.

Cameron Woo is a freshman at the University of

Pennsylvania in the School of Engineering and Applied

Science. Originally from New Jersey, he is an alum of the

2019 Summer Science Program in Astrophysics at the

University of Colorado Boulder.

Wilson Zheng hails from Shanghai, China and a high

school senior at Berkshire School. He is also an alum of the

2020 Summer Science Program in Astrophysics.


Autumn 2020



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