SkyShot - Volume 1, Issue 1: Autumn 2020

SkyShot Autumn 2020
Supercomputing for Analyzing
Hypernovae and Neutron Star
Mergers
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].
Conclusion
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
worldwide.
Citations
[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
https://phys.org/news/2019-06-ai-universe-sim-fast-accurateand.html
[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.
doi:10.1093/mnras/staa2498
[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
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