IRAM-30m HERA time/sensitivity estimatorJ. Pety 1,2 , M. Gonzá<strong>le</strong>z 3 , S. Bardeau 1 , E. Reynier 11. IRAM (Grenob<strong>le</strong>)2. Observatoire de Paris3. IRAM (Granada)Feb., 18th 2010Version 1.0AbstractThis memo describes the equations used in the IRAM-30m HERA time/sensitivity estimator availab<strong>le</strong>on the IRAM-30m web page. A large part of the memo aims at describing the peculiarities oftime/sensitivity estimation of the On-The-Fly observing mode with a multi-pixel like HERA. It explainshow to generalize the equations of the sing<strong>le</strong> pixel case, so that the same code can be used inboth cases (sing<strong>le</strong> and multi-pixel).<strong>tel</strong>-<strong>00726959</strong>, version 1 - 31 Aug 2012IRAM-30m HERA time/sensitivity estimatorIn these formulas2. generalization to a multi-pixel receiver• η<strong>tel</strong> is the efficiency of the <strong>tel</strong>escope. It includes the time needed 1) to do calibrations (e.g. pointing,focus, temperature sca<strong>le</strong> calibration), and 2) to s<strong>le</strong>w the <strong>tel</strong>escope between useful integrations, etc...Its value is decided by IRAM: It should not be changed by the PI.• ηspec is the spectrometer efficiency.• dν is the frequency resolution.• npol is the number of polarizations tuned at the same frequency (1 or 2).• Tsys is the system temperature, which is a summary of the noise added by the system. It is usual toapproximate it (in the Ta⋆ sca<strong>le</strong>) with(1 + Gim) exp {τs A}Tsys = [Feff Tatm (1 − exp {−τs A}) + (1 − Feff) Tcab + Trec] , (3)Feffwhere Gim is the receiver image gain, Feff the <strong>tel</strong>escope forward efficiency, A = 1/ sin(e<strong>le</strong>vation)the airmass, τs the atmospheric opacity in the signal band, Tatm the mean physical atmospherictemperature, Tcab the ambient temperature in the receiver cabine and Trec the noise equiva<strong>le</strong>nttemperature of the receiver and the optics.• nbeam is the number of independent measurement in the map observed in the OTF mode. It is givenbynbeam = A mapAbeamwithηgrid π θ2Abeam =4 ln(2) . (4)where A map is the map area, Abeam is the area of the resolution e<strong>le</strong>ment in the map, ηgrid is thesmoothing factor due to gridding and θ is the <strong>tel</strong>escope full width at half maximum.• nsubmap the number of submaps needed to cover the who<strong>le</strong> map area, a submap being the areacovered between two successive off measurements. nsubmap is computed withnsubmap =A mapAsubmapwith Asubmap = θ2.5 v linear tstab<strong>le</strong> (5)where v linear is the <strong>tel</strong>escope scanning speed and tstab<strong>le</strong> is the typical timesca<strong>le</strong> of stability of theobserving system.The demonstrations and additional subt<strong>le</strong>ties for the OTF case are fully described in Pety et al. (2009).2 Generalization to a multi-pixel receiver2.1 Description of HERA, the IRAM-30m multi-pixelsHERA is a multi-pixel receiver working at 1 mm of wave<strong>le</strong>ngth. Each pixel is an heterodyne mixer usinga SIS junction. There are nine pixels per polarization. The pixels of one polarization follow a 3 × 3 squarepattern, the distance between two pixels being ∆ = 24 ′′ . Both polarizations are aligned. Hence, HERAhas 18 pixels in total looking at 9 different sky position simultaneously. The polarizations of HERA cansimultaneously be tuned at two different frequencies.The number of used polarization, npol can thus be set to 1 or 2 and the number of pixels per polarizationis npix = 9.1 Summary of the formulas for a sing<strong>le</strong> pixel receiverWe summarize here the relations between the rms noise (σ) and the elapsed <strong>tel</strong>escope time (t<strong>tel</strong>) derivedby Pety et al. (2009) in the case of a sing<strong>le</strong>-pixel heterodyne receiver. The results depends on a combinationof• The observation kind:Tracked observations where the <strong>tel</strong>escope track the source, i.e. it always observes the sameposition in the source referential. The result is a sing<strong>le</strong> spectra.On-The-Fly observations where the <strong>tel</strong>escope continuously s<strong>le</strong>w through the source with time tomap it. The result is a cube of spectra.• and of the switching mode:Position switch where the off-measurement is done on a close-by sky position devoid of signal.Wobb<strong>le</strong>r switching is a particular case.Frequency switch where the <strong>tel</strong>escope always points towards the the source and the switching isdone in the frequency (velocity) space. In this case,The formulas are• for tracked observationsσpsw track 2 Tsys=ηspec√dν npol η<strong>tel</strong> t<strong>tel</strong>• for OTF observations(√σpsw otf nbeam + √ )nsubmap Tsys= √ηspec dν npol η<strong>tel</strong> t<strong>tel</strong>IRAM-30m HERA time/sensitivity estimator2.2 An average pixel, and σ trackfsw =1, and σ otffsw =√2 Tsys. (1)ηspec√dν npol η<strong>tel</strong> t<strong>tel</strong>√ 2 nbeam Tsys. (2)ηspec√dν npol η<strong>tel</strong> t<strong>tel</strong>2. generalization to a multi-pixel receiverThe scatter of the mixer performances, which translate into a scatter of receiver temperatures, is the firstthing to deal with. Instead of computing the sensitivity associated with each mixer, we introduce anaverage pixel, which will represent all the other ones. In Eqs. 1 and 2, the caracteristics of the mixersare hidden into the system temparature, Tsys. We will thus define an average system temperature, T sys,which will represent the receiver average pixel.Among the different ways to define such an average system temperature, we privi<strong>le</strong>dge the one whichwill give the right sensitivity in the case where the same point of the sky is seen by all the differentpixels. This choice is made because 1) the same point of the sky is at <strong>le</strong>ast seen by two pixels (one perpolarization) and 2) it is a good idea when mapping to try to cover the mapped area as many time aspossib<strong>le</strong> with sligthly different observing configuration of HERA (e.g. rotations by 90deg) to homogenizethe noise distribution and to ensure that bad pixels see different part of the mapped area.It is well-known that the optimal way to combine (e.g. to average or to grid) spectra is to weight themby w = 1/σ 2 before combination, where σ is their rms noise. In this case, it can be shown that the weightof the combination is the linear sum of the weights. From this, it is easy to define T sys as2.3 Impact on tracked observations∑npol npix1T 2 =T 2 . (6)sys i=1,npol, sys j=1,npix ijDuring tracked observations, each pixel of one polarization will look at a different position of the sky, butalways the same position with time. We thus simply have to change Tsys by T sys in Eqs. 1, i.e.σpsw track =√2 T sys2 T, and σηspec√ sysfsw =. dν npol η<strong>tel</strong> t<strong>tel</strong>ηspec√dν npol η<strong>tel</strong> t<strong>tel</strong>(7)2.4 Imaging with HERAHERA has a derotator, which ensures that the pixels do not rotate on the sky. The sky can thus bemapped by scanning along e.g. the right ascension or the declination axis in equatorial coordinates. Weaim at obtaining a fully samp<strong>le</strong>d map, implying a distance between the rows of ∆ = θ/2.5, where θ is thebeam full width at half maximum: At 1 mm, this corresponds typically to 4 ′′ . However, the pixels aretypically separated by ∆ ≃ 2θ. We thus have to find the best scanning strategy which will fill the ho<strong>le</strong> ofthe instantaneous footprint of the multi-pixel. To do this, we will use a property of the deroratator, i.e.it can be configured so that one of the main axes of the multi-pixel is rotated by an ang<strong>le</strong> (α) from thescanning direction. Indeed, we can ask what is the value of α needed so that the distance between therows of two adjacent pixels is exactly ∆. For a receiver of √ npix × √ npix pixels, we end up with √ npixgroups of lines, the distance between two group of lines being noted δ ′ . A bit of geometry givesIf we now impose thatwe obtainδ = ∆ sin α and δ ′ = ∆ cos α. (8)δ ′ = nsubscan√ npix δ, (9)1tan α = √ . (10)nsubscan npixWe can fully samp<strong>le</strong> without redundancy a given fraction of the sky in a sing<strong>le</strong> subscan (nsubscan = 1) orin two paral<strong>le</strong>l subscans (zigzag, nsubscan = 2).For HERA, √ npix = 3 and the ∆ value is fixed to 4 ′′ by the observing wave<strong>le</strong>nght ∼ 1 mm. nsubscan = 1gives α = 18.4 ◦ , ∆ ∼ 12 ′′ and nsubscan = 2 gives α = 9.5 ◦ , ∆ ∼ 24 ′′ . Current optical design implies aminimum distance between the pixels which is only compatib<strong>le</strong> with the nsubscan = 2 solution.23
IRAM-30m HERA time/sensitivity estimator 2. generalization to a multi-pixel receiverIRAM-30m HERA time/sensitivity estimator2. generalization to a multi-pixel receiver<strong>tel</strong>-<strong>00726959</strong>, version 1 - 31 Aug 2012In summary, by setting an ang<strong>le</strong> of 9.5 ◦ between one of the main axes of a 3 × 3 multi-pixels and thescanning direction, we can sweep in a fully samp<strong>le</strong>d mode a given portion of the sky with two paral<strong>le</strong>lscans separated by 3δ = 12 ′′ . The region of the sky fully samp<strong>le</strong>d will then be rectangular: the <strong>le</strong>ngth ofthe rectangular side perpendicular to the scanning direction is then d⊥ = nsubscannpixδ, whi<strong>le</strong> the <strong>le</strong>ngth ofthe rectangular size paral<strong>le</strong>l to the scanning direction, d ‖, will depend on the observing strategy. However,there is an edge effect, due to the rotation of the array from the scanning direction. Indeed, the edges ofthe maps are not fully samp<strong>le</strong>d: Thus must thus be considered as overheads. The area of the scanned skymust thus be larger than the targeted area, which must be fully samp<strong>le</strong>d. Let’s assume that the targetedarea (Atarget) is swept as a succession of n⊥ rectang<strong>le</strong>s of size d⊥ × d ‖. We getAtarget = n⊥ d⊥ d ‖. (11)The area swept in the under-samp<strong>le</strong>d edges (Aedge) is just the area of the rectang<strong>le</strong> whose side sizes aren⊥ d⊥ and the scanning size of multi-pixel rotated by α, i.e.dedge = ( √ npix − 1) ∆ (cos α + sin α) (12)Indeed, the geometry of the edges show that half this area is covered on each size of the targeted area.Using Eqs. 8 and 9, we obtainWe now define the mapping efficiency ηedge asηedge =dedge = ( √ npix − 1) (1 + nsubscan√ npix)δ (13)Atarget,Atarget + Aedgewith Aedge = n⊥ d⊥ dedge. (14)Replacing Atarget and Aedge by their expressions 11 and 14, we derive=1ηedge =1. (15)1 + dedged ‖1 + dedgea n⊥ d⊥This expression indicates that the most efficient mapping strategy is to observe very wide scans. However,avoiding the edge overheads is only one aspect of wide-field mapping with a multi-pixels. In particular, weaim at having the most homogeneous map as possib<strong>le</strong>. To achieve this, we need to scan as fast as possib<strong>le</strong>so that the observing conditions are as comparab<strong>le</strong> as possib<strong>le</strong> on the who<strong>le</strong> map. We can then repeat themap as many time as possib<strong>le</strong> so that the data affected by technical prob<strong>le</strong>ms or bad weather happeningduring one coverage can just be discarded. In any case, at <strong>le</strong>ast two coverages obtained in perpendicularscanning direction is always advise to be ab<strong>le</strong> to use destriping algorithms (e.g. plait algorithms). Stripeshappen because the system stability (weather, <strong>tel</strong>escope, receiver and backend) evolves from one row tothe other. Getting stripes is all the more probab<strong>le</strong> than the time to scan a row is long. So this arguesagainst making very wide scans, which are at the same time required to decrease the relative time spentin the edge overheads. A compromise is thus to map area chunks which are as close as possib<strong>le</strong> to squares.A way to parametrize this is to introduce the map aspect ratio, defined asa =d ‖n⊥ d⊥with a > 1 and n⊥ integer. (16)A given area A map will be mapped in chunks whose area (Achunk) is defined by the linear scanningspeed and the time of stability of the system (tchunk). This givesUsing 16 to replace d ‖ by a n⊥ d⊥, we yieldn⊥ d⊥ (d ‖ + dedge) = Achunk with Achunk = v linear d⊥ tchunk. (17)n 2 dedge⊥ + n⊥a d⊥IRAM-30m HERA time/sensitivity estimatorStep #2: Computation of n⊥ and aCase Atarget < η minedge4− Achunka d 2 = 0. (18)⊥2. generalization to a multi-pixel receiverdedge = ( √ npix − 1) (1 + nsubscan√ npix)δ, (25)t pswchunk = 2 minutes and tfsw chunk = 10 minutes. (26)Achunk = θ 4 fdumpd⊥tchunk. (27)nsubscanAchunk with ηminedge = 0.8[√ ]Atarget1. n⊥ = floord⊥, (28)2. if n⊥ = 0, then send an error message: “Area too small, use raster mapping.”, (29)3. a = Atarget(n⊥ d⊥) 2 . (30)Case Atarget ≥ η minedge Achunk{ [√]}1 dedge1. n⊥ = floor1 + 4Achunk2 d⊥d 2 − 1 , (31)edge2. if n⊥ = 0, then send an error message: “Area too small, use raster mapping.”, (32)3. a = Achunk(n⊥ d⊥) 2 − dedge . (33)n⊥ d⊥Step #3: Computation of ηedge1ηedge =. (34)1 + dedgea n⊥ d⊥Step #4: Recomputation of Achunk and tchunk when Atarget < η minedge Achunk1. A newchunk = Atarget , (35)ηedge2. t new A newchunkchunk = tchunk , (36)Achunk3. Achunk = A newchunk newchunk. (37)and tchunk = tIf tchunk < 1 minute, the targeted area is too small and the PI should use raster mapping instead ofOTF mapping.2.5 Impact on OTF observationsFor OTF observations, there are several effects to take into account.1. We will use the average system temperature to take into account the different mixer performances.2. Edges result in inhomogeneous noise, which depends on the exact observing setup. We here try toestimate a sing<strong>le</strong> noise value for the who<strong>le</strong> map. The area swept in edges are thus considered asoverheads. If the total targeted area is A map , the receiver will then have to map A map + Aedge. Asdiscussed above, we can write the previous sum as a product of the targeted area times an efficiencyfactor, i.e.ηedge (A map + Aedge) = A map . (38)Tab<strong>le</strong> 1: Mapping strategy to minimize edge effects.tchunk n⊥ a ηedgemin.1 1 3.7 0.832 2 1.9 0.835 4 1.2 0.8610 6 1.1 0.90This equation of the 2nd order has only one physical solution[√]n⊥ = 1 dedge 4a Achunk1 +2 a d⊥d 2 − 1 . (19)edgeWe note that this yieldswitha Achunkd 2 edge= θ [(4δ √nsubscannpix −1ηedge =21 + √1+ 4a A chunkd 2 edge1√ nsubscan npix−1(20)a fdumptchunk) ( )] 2 . (21)√nsubscan 1− − √ nsubscanThis expression can be used to understand how to get the highest mapping effiency (ηedge). This impliesto get the largest value of the (a Achunk)/d 2 edge ratio. We see that the larger the multi-pixel array, thesmal<strong>le</strong>r this ratio. Increasing the chunk area, either by increasing the linear velocity (i.e. increasing thedump rate, fdump) or by increasing the stability time (tchunk) will increase the efficiency. The dump rateis fixed by the peak data rate, which gives typically fdump = 2 Hz. The stability time depends on theswitching mode: It is the time between two off measurements in position switch (typically 1 or 2 minutes)and the time between two calibrations in frequency switch (typically 10 to 15 minutes).Previous equations give the impression that the aspect ratio is a free parameter. This is not fully truebecause, n⊥ must be an integer. The following algorithm ensures that we get an integer value for n⊥ withthe value of a > 1 and closest to 1. Starting with a = 1, Eq. 18 gives a value of n⊥. We enforce the integernature of n⊥ withn⊥ = floor(n⊥), (22)and we recompute the associated aspect ratio witha =Achunk(n⊥ d⊥) 2 − dedge . (23)n⊥ d⊥Tab<strong>le</strong> 1 gives the resulting values of n⊥, a and ηedge as a function of the stability time (tchunk). We seethat edge efficiencies are quite high. However, it is easier to have square chunks when the stability time islarger.In summary, the time spent in edges is counted as overheads. It translates into a multiplicativeefficiency (ηedge) because we enforce a mapping pattern through rectangular chunks. Although it is notintuitive (edge sizes are in general unrelated to area), this is not a big assumption because the use of asquare multi-pixel anyway enforces mapping in rectangular chunks. We now summarize the algorithm tocompute ηedge:Step #1: Computation of input quantitiesIRAM-30m HERA time/sensitivity estimatord⊥ = nsubscan npix δ, (24)5ReferencesWe thus have to remplace A map by A map /ηedge in Eqs 4 and 5 to compute nbeam and nsubmap. Now,if edge area is considered overheads when estimating the sensitivity, the spectra acquired in the edgeswill neverthe<strong>le</strong>ss be used to form the final image. We must thus ensure that enough time is observedon the off position when estimating the sensitivity in the position switch mode. This comes naturallyif we consider the edge area as part of the submap between two off positions. This implies that thechange on the total mapped area, expressed above, is the only one needed in the equations to takethe edges into account.3. A multi-pixel can cover npix times as fast the same area of the sky with the same sensitivity as asing<strong>le</strong>-pixel of similar T sys. Another way to look at this, is to assume that each identical (average)pixel will cover an independent part of the sky during a given observing time (i.e. η<strong>tel</strong> t<strong>tel</strong>). Thisimplies that the area seen by each pixel will beThis finally givesσ otfpsw =(√ √ )n pixbeam + n pixsubmap T sysηspec√dν npol η<strong>tel</strong> t<strong>tel</strong>where n pixbeam and npixsubmap are computed withn pixbeam = A mapηedge npix AbeamwithA pixmap = A map/ηedge. (39)npixand, and σ otffsw =The times spent on and off and in the edges per pixel are thenn pixsubmap = A mapηedge npix A pixsubmap√2 n pixbeamηspec√ T sys , (40)dν npol η<strong>tel</strong> t<strong>tel</strong>(41)A pixsubmap = vpix area tstab<strong>le</strong> and v pixarea = δ v linear . (42)t pixonoff = ηedge η<strong>tel</strong> t<strong>tel</strong> and tpixedge = (1 − ηedge) η<strong>tel</strong> t<strong>tel</strong>. (43)The algorithm to derive the time/sensitivity estimation in the case of OTF can thus be applied withthe following modifications in the input parameters : tonoff, v area , nsubmap, nbeam must be replaced byt pixonoff , vpix area, n pixsubmap , npix beam .ReferencesPety, J., Bardeau, S. and Reynier, E., 2009, IRAM-30m EMIR time/sensitivity estimator, IRAM Memo2009-167
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UNIVERSITÉ PIERRE ET MARIE CURIEHA
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Table des matières1 Rapport de sou
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3 REQUIREMENTS 44 CHANGES FOR END-U
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5 CHANGES FOR PROGRAMMERS 125 CHANG
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A EXHAUSTIVE DESCRIPTION OF THE CHA
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Chapitre 9Copyright: Stéphane Guis
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Contribution de l'Action Spécique
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A. des multi-pixels à bure : une s
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5. besoins en services annexes, bé
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Articles publiés dans des revues
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ARTICLES PUBLIÉS DANS DES REVUES
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Mémos IRAM et ALMAtel-00726959, ve
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Actes de colloques nationaux et int
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ACTES DE COLLOQUES NATIONAUX ET INT
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