ApJ

15 Pages·2016·5.84 MB·English

by  I. Baronchelli

The Astrophysical Journal Supplement Series, 223:1(15pp), 2016 March doi:10.3847/0067-0049/223/1/1
© 2016. The American Astronomical Society. All rights reserved.
THE SPITZER-IRAC/MIPS EXTRAGALACTIC SURVEY (SIMES)
IN THE SOUTH ECLIPTIC POLE FIELD
I.Baronchelli1,2,C.Scarlata1,G.Rodighiero2,A.Franceschini2,P.L.Capak3,S.Mei3,4,5,M.Vaccari6,7,L.Marchetti8,
P. Hibon9, C. Sedgwick8, C. Pearson8,10,11, S. Serjeant8, K. Menéndez-Delmestre12, M. Salvato13, M. Malkan14,
H. I. Teplitz3, M. Hayes15, J. Colbert3, C. Papovich16, M. Devlin17, A. Kovacs1,3, K. S. Scott18, J. Surace3,
J. D. Kirkpatrick3, H. Atek19, T. Urrutia20, N. Z. Scoville3, and T. T. Takeuchi21
1MNInstituteforAstrophysics,UniversityofMinnesota,116ChurchStreetSE,Minneapolis,MN55455,USA
2DipartimentodiFisicaeAstronomia,UniversitàdiPadova,vicoloOsservatorio,3,I-35122Padova,Italy
3CaliforniaInstituteofTechnology,1200E.CaliforniaBoulevard,Pasadena,CA91125,USA
4GEPI,ObservatoiredeParis,PSLResearchUniversity,CNRS,UniversityofParisDiderot,61,Avenuedel’ObservatoireF-75014,Paris,France
5UniversityofParisDenisDiderot,UniversityofParisSorbonneCité(PSC),F-75205ParisCedex13,France
6AstrophysicsGroup,PhysicsDepartment,UniversityoftheWesternCape,PrivateBagX17,7535Bellville,CapeTown,SouthAfrica
7INAF—IstitutodiRadioastronomia,viaGobetti101,I-40129Bologna,Italy
8DepartmentofPhysicalSciences,TheOpenUniversity,MiltonKeynes,MK76AA,UK
9GeminiSouthObservatory,Casilla603,LaSerena,Chile
10RALSpace,RutherfordAppletonLaboratory,Chilton,Didcot,OxfordshireOX110QX,UK
11OxfordAstrophysics,DenysWilkinsonBuilding,UniversityofOxford,KebleRoad,OxfordOX13RH,UK
12ObservatóriodoValongo,UniversidadeFederaldeRiodeJaneiro,RiodeJaneiro,Brazil
13MaxPlanckinstituteforextraterrestrialPhysics,Giessenbachstrasse1,Garching,D-85748,Germany
14DepartmentofPhysicsandAstronomy,UCLA,PhysicsandAstronomyBuilding,3-714,LACA90095-1547,USA
15DepartmentofAstronomy,OskarKleinCentre,StockholmUniversity,AlbaNovaUniversityCentre,SE-10691Stockholm,Sweden
16DepartmentofPhysicsandAstronomy,TexasA&MUniversity,CollegeStation,TX77843-4242,USA
17DepartmentofPhysicsandAstronomy,UniversityofPennsylvania,209South33rdStreet,Philadelphia,PA19104,USA
18NationalRadioAstronomyObservatory,520EdgemontRoad,Charlottesville,VA22903,USA
19Laboratoired’Astrophysique,EcolePolytechniqueFédéraledeLausanne,ObservatoiredeSauverny,CH-1290Versoix,Switzerland
20LeibnizInstitutfürAstrophysikPotsdam,AnderSternwarte16,D-14482Potsdam,Germany
21DivisionofParticleandAstrophysicalScience,NagoyaUniversity,Furo-cho,Chikusa-ku,Nagoya464-8602,Japan
Received2015September18;accepted2016January2;published2016March7
ABSTRACT
WepresenttheSpitzer-IRAC/MIPSExtragalacticsurvey(SIMES)intheSouthEclipticPolefield.Thelargearea
covered(7.7deg2),togetherwithoneofthelowestGalacticcirrusemissionsintheentireskyandaveryextensive
coverage by Spitzer, Herschel, Akari, and GALEX, make the SIMES field ideal for extragalactic studies. The
elongated geometry of the SIMES area (≈4:1), allowing for significant cosmic variance reduction, further
improves the quality of statistical studies in this field. Here we present the reduction and photometric
measurements oftheSpitzer/IRAC data.Thesurveyreachesdepthsof1.93and1.75μJy(1σ)at3.6and4.5μm,
respectively. We discuss the multiwavelength IRAC-based catalog, completed with optical, mid-, and far-IR
observations.Wedetect341,000sourceswithF  3s.Ofthese,10%haveanassociated24μmcounterpart,
3.6mm
while 2.7% have an associated SPIRE source. We release the catalog through the NASA/IPAC Infrared Science
Archive. Two scientific applications of these IRAC data are presented in this paper. First, we compute integral
number counts at 3.6μm. Second, we use the [3.6]–[4.5] color index to identify galaxy clusters at z > 1.3. We
select 27 clusters in the full area, a result consistent with previous studies at similar depth.
Key words: catalogs – galaxies: evolution – infrared: galaxies – submillimeter: galaxies – surveys
1. INTRODUCTION 24 and 500μm. The Herschel satellite with its PACS and
SPIRE instruments (Griffin et al. 2010; Pilbratt et al. 2010;
Contrary to the expectation that galaxy formation would
proceed via merger-driven bursts of star formation (SF), Poglitsch et al. 2010) have revolutionized the field, producing
large samples of mid-IR bright galaxies, selected up to very
evidence is now overwhelmingly showing that the bulk of SF
intheuniversehappenedina“quiescent”mode,atincreasingly largeredshiftsviatheirbolometricluminosity(e.g.,Rodighiero
higher average rates at earlier cosmic times (e.g., Daddi et al. et al. 2010, 2011; Wuyts et al. 2011; Magnelli et al. 2012;
Oliver et al. 2012; Rosario et al. 2012; Gruppioni et al. 2013;
2007; Noeske et al. 2007; Peng et al. 2010; Rodighiero
et al. 2011). Although short-lived powerful merger-driven Santinietal.2014).Tounderstandthephysicalnatureofthese
starbursts (SFR > 1000Meyr−1) do not contribute signifi- sources,however, detailed samplingof thefullspectral energy
cantlytotheslowprocessofgalaxygrowth,theymayrepresent distribution (SED) and (spectroscopic/photometric) redshifts
a critical phase in the structural transformation and quenching are needed, and require a secure counterpart association at
of the most massive galaxies (e.g., Rodighiero et al. 2011; shorter wavelengths. With secure counterparts and sufficient
Bedregal et al. 2013). ancillary data, accurate photometric redshifts can be computed
ThemostactivelySFgalaxiesatanyredshiftalsotendtobe that will allow the effective use of the Atacama Large
the most dust-obscured objects. They disappear at rest-frame Millimeter/submillimeter Array, not only to measure spectro-
UV wavelengths and emit most of their energy in the far-IR scopicredshifts, butalsotounderstand thephysicalconditions
where they can be easily identified through imaging between of the molecular gas reservoir in these objects.
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TheAstrophysicalJournalSupplementSeries,223:1(15pp),2016April Baronchellietal.
In this paper, we present the Spitzer-IRAC/MIPS Extra- The field was covered in two visits, between 2011 November
galactic survey (SIMES), an infrared survey carried out with 16 and 23, in order to facilitate identification and removal of
the Spitzer Space telescope (Fazio et al. 2004b; Werner et al. asteroids.Thefirstandsecondvisitsconsistedof3×30sand
2004)ina7.7deg2fieldclosetotheSouthEclipticPole(SEP) 2×30sframes,respectively,obtainedwithamediumcycling
at 3.6 and 4.5μm. The SIMES field, centered at (α, ditherpattern,foratotalexposuretimeof150s.Thereduction
δ) = (4h44m, −53°30′), has Galactic cirrus emission among oftheIRACdatagenerallyfollowedtheprocedureusedbythe
the lowest in the entire sky (∼2–3 MJystr−1 at 100μm, Spitzer Enhanced Imaging Products (SEIP) mosaic pipeline
Schlegel et al. 1998; Matsuhara et al. 2006), thus minimizing (Capak et al. 2013) with one additional step. Before the SEIP
the extinction in the UV and optical bands as well as mosaicprocessing, amedianimage wascreated for eachAOR
maximizing the sensitivity at far-IR wavelengths. This field is (observing block) and subtracted from the frames to remove
therefore very appealing for full multiwavelength exploitation. residual bias in the frames and persistence from previous
Furthermore, it has the unique advantage of having an observations. The MOPEX Overlap routine (Makovoz
elongated geometry(axialratioofapproximately ∼4:1),which et al. 2005) was then used on the background subtracted
minimizes the cosmic variance compared to square fields of images to remove any residual background variation from
similar depth and area on the sky (Trenti & Stiavelli 2008). frame to frame. The median subtracted frames were then
The SIMES field has been the target of a vast array of combined with the MOPEX mosaic pipeline (Makovoz &
multiwavelength observing programs from major observa- Khan2005).Theoutlierandbox-outliermoduleswereusedto
tories: Spitzer (Clements et al. 2011), Herschel (Oliver rejectcosmicrays,transients,andmovingobjects.Manyofthe
et al. 2012; Wang et al. 2014), GALEX (Dale et al. 2007). In exposures were affected by latent images from prior observa-
particular, together with the North Ecliptic Pole area, it is one tions of a bright object, although the impact was much more
ofthetwofieldsincludingthedeepestcontiguousobservations severe in channel 1 than in channel 2 where it effectively
by theAkari IR observatoryin thecontextof the AKARI Deep doubled the noise over the background-limited estimate. The
Field South survey (Matsuhara et al. 2006; Matsuura effects of these latent images could not be fully mitigated
et al. 2011). These observations provide us with the most because they faded during the observations, and so a perfect
extensive photometric coverage in the mid-IR available for modelcouldnotbeproduced.Asaresult,the3.6μmdatahave
cosmologicalsurveys,ofparticularrelevancefortheanalysisof asensitivitycomparabletothe4.5μmdata(seeSection2.2and
dust-obscured active galaxies and AGNs. Very important for Table2).Asafinalstep,thedataweretheninterpolatedontoa
the identification at IR and optical wavelengths is the 0 6pixel scale using a linear interpolation and combined with
availability of Spitzer24μm imaging (Rieke et al. 2004), an exposure time weighted mean combination. Mean, median,
which, with its 5 0 beam, nicely links imaging at shorter and coverage, uncertainty, and standard-deviation images were
longer wavelengths. Until our survey, however, the SIMES created.Thefinalresultingmosaicisshowninthetoppanelof
field was missing the crucial IRAC coverage required to Figure 1, while the bottom panel shows a comparison of the
associate the majority of broad-beamed/confused 24μm IRAC coverage with the coverage in the MIPS, SPIRE, and
sources with physically understood astrophysical objects optical surveys within the same area.
(detected at λ<1μm). Here we present the new IRAC
observations of the SIMES field, targeting this outstanding
2.1. Source Extraction and Photometry
wavelength gap and thus allowing the full exploitation of the
available longer wavelength data. These data will also be For the detection and extraction of sources we used the
crucial for the measurement of physical properties of high- SExtractor software (Bertin & Arnouts 1996) in dual image
redshift objects, including their photometric redshifts and mode, using the 3.6μm map as the detection image and the
stellar masses. uncertainty map as a weight image. During the detection step,
The paper is organized as follows. We describe the IRAC we used a local background calculated over an area of
observations and catalog preparation in Section 2. The 32×32pixels filtered with a 3pixel top-hat kernel. We set a
matching with the long wavelength ancillary data is described 1.5σ threshold, with a minimum of 5 connected pixels above
in Section 3. Finally, we discuss initial results on the 3.6μm the background noise. For each object we computed AUTO
number counts and the identification of intermediate-redshift totalfluxes,aswellasaperturefluxesmeasuredinaperturesof
galaxyclustersinSection4.Throughoutthispaper,weassume 4 8, 7 2, and 12 0 diameter.
a standard flat cosmology with H0 = 70Kms−1Mpc−1, SExtractor AUTO fluxes are estimates of the total flux of a
ΩM = 0.3, and ΩΛ = 0.7. Wherever magnitudes or colors source in an elliptical aperture with semimajor axis (a)
are reported, the AB magnitude system is implicitly assumed. proportional to the Kron radius of the object (R , Kron 1980).
K
We chose a = 2.5 R by setting the SExtractor parameter
K
Kron_fact = 2.5.22 This choice ensures that the aperture
2. OBSERVATIONS AND DATA ANALYSIS
includes more than the 90% of the total galaxy flux23
SIMESisaSpitzerCycle8GeneralObserverprogram(PID (Kron 1980). For apertures with a<3.5pixels (2 1), the
80039,P.I.:Scarlata)observedduringthewarmmissionphase. AUTO flux is computed within a circular aperture. AUTO
The SIMES survey in the IRAC bands was designed with the fluxes account for the real apparent dimension of each source,
goal of complementing the existing MIPS 24μm and far-IR
observations.The7.74deg2fieldwascoveredin78hrwiththe 22 In SExtractor nomenclature, a = KRON_RADIUS×A_IMAGE, where
IRAC instrument in both channels 1 and 2, corresponding to A_IMAGE is the luminosity profile rms, in pixels, along the major axis
imaging at 3.6μm and 4.5μm, respectively. In order to direction, while KRON_RADIUS = Kron_fact×RK, with RK in units of
efficientlycovertheelongatedregion,weusedmultiple4×16 A_IMAGE.
23 TheKronapertureincludesadifferentfractionofthetotallightofagalaxy,
IRAC AORs. This strategy was chosen to minimize the effect dependingonthevalueoftheSérsicindexnofitssurfacebrightnessprofile
of the substantial rotation (1 deg per day) at the field latitude. (Sérsic1963;Graham&Driver2005).
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Figure1.Toppanel:IRAC3.6μmmosaicoftheSIMESfield.ThecolorscaleisinunitsofMJysr−1.Bottompanel:Coveragemapat3.6μm.Thecolorscaleshows
thenumberofframesperpixel.TheareascoveredbyMIPS,SPIRE,andWFI-R (optical)areshowninred,cyan,andblack,respectively.
c
the elliptical shape of the observed isophotes and the source’s There is a strong linear correlation between logáRñ and the
radial surface brightness profile. semimajor axis of the elliptical aperture, with different slopes
InFigure2,wereportthemeanratioáRñbetweentheAUTO depending on the size of the circular aperture used. The ratio
fluxesandtheAPERTUREfluxescomputedforoursourcesin becomes∼1 when the semimajor axis of the elliptical aperture
bins of semimajor axis. áRñ increases with the increasing hasadimensionsimilartothatofthecircularapertureusedfor
apparent dimensionofthesources,indicatingthatafractionof the comparison. When considering the smallest elliptical
the sources’ emission is missed when using a fixed aperture. apertures and the largest circular apertures, we observe a large
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Figure3.Cumulativedistributionofthepixelcoverage.Morethan70%ofthe
pixelsinthemosaicareatthenominal150scoverage.
Figure2.AverageratiobetweenAUTOfluxesanduncorrectedaperturefluxes
(4 8inblack,7 2inred,12 0ingreen),asafunctionofthesemimajoraxis
of the Kron elliptical aperture, together with best linear fit coefficients,
expressed aslogáRñ=a´(semimajor axis) + b.The figure shows how an
increasingamountofthesource’semissionismissedwhenusingfixedcircular background-dominatedapertures.Thestandarddeviationofthe
aperturestocomputefluxes. best-fit Gaussian distribution in each bin of coverage is then a
measurement of the average background noise corresponding
deviation from áRñ ~ 1. This effect is likely due to an to that coverage.
overestimate of the background that becomes appreciable To check the normal distribution of the pixels noise, we
when the elliptical and circular apertures have very different applied both the Kolmogorov–Smirnov (K–S) and Anderson–
sizes.Sourcescharacterizedbysmallapparentdimensions(i.e., Darling (A–D) tests to the negative side of the pixel flux
small semimajor axis of the elliptical aperture) tend to have distribution. Because the noise level is expected to depend on
smalleraperturefluxesforlargeraperturesizes(greencurvesin the exposure time, we consider seven equally sized bins of
Figure 2). Thus, hereafter, all fluxes reported are total fluxes coverage ranging between 2 and 10. At the nominal coverage
“FLUX_AUTO” measured within the Kron SExtractor of 5, using the K–S test, the probability of finding the
apertures. computed difference D = 3.9×10−3 between the cumulative
distribution of fluxes and that expected from a normal
distributionis0.74.UsingtheA–Dtest,wefoundadifference
2.2. Survey Sensitivity
of A2 = 0.55, which is close to the reference value of 0.576,
ThemappingstrategyadoptedtocoverthelargeSIMESarea corresponding to the rejection of the null hypothesis (normal-
results in varying coverage across the field with a resulting ity) with a 15% level of significance. At lower and higher
noise variation. Figure 3 shows the cumulative distribution of coverages, the probability from the K–S test ranges from 0.28
thepixelcoverageinthe3.6μmmosaic,wherethecoverageon to0.96,whileA2rangesfrom0.49to1.87,indicatingthatwhen
the horizontal axis is defined as the number of 30s exposures a deviation from the normal distribution is present, it is small.
per pixel. More than 70% of the mosaic is at or above the InFigure4,weshowtheresultingσasafunctionofaverage
planned coverage of 150s. coverageáCñineachofthesixbins.ThedependencywithCis
Inordertoidentifyreliabledetectionsinthe3.6μmcatalog, inagreementwiththePoissonianapproximation(shownwitha
we follow Surace et al. (2005) and compute a coverage-based continuousredline).Thetheoreticalcurveisnormalizedatthe
signal-to-noiseratio(S/N)foreachsource.Suraceetal.(2005) nominal coverage of the survey (C = 5). The trend between σ
compute the noise σ corresponding to the mean coverage and C is well reproduced by the relation s µ C-a, with
withinthemosaic(áCñ,whereCisthenumberofexposuresper α=0.43±0.09(greendashedlineinFigure4).Tocalculate
pixel) and then scale it by a factor f that accounts for the the S/Ns, we computed APERTURE flux and noise in the
specific coverage, C, of the aperture used for the flux same aperture (4 8 diameter). For each source, the underlying
measurement, i.e., f = áCñ C. This procedure assumes that coverage is computed as the median value in the aperture. We
the noise scales as the square root of the exposure time. retain in the final catalog only sources with 3.6μm flux above
However, thenoise contribution from faint unresolved sources 3σ. The final IRAC-based catalog constructed in this way
could be substantial in the deepest regions of the mosaic. includes 341,006 sources.
In order to check the t-0.5 assumption, we empirically Wecomputethesensitivityofthe4.5μmobservationsusing
exp
derived the noise properties of the mosaic as a function of the the same method described for the 3.6μm data. Again,
actualcoverage.First,wemeasuredthefluxin8pixeldiameter measuring σ as a function of the average coverage, we found
apertures distributed in a homogeneous grid covering the an agreement with the Poissonian expectation, with α
mosaic. Then, we divided our measures in different groups, (4.5μm) = 0.53±0.08.
according to the coverage underlying the apertures in which At all coverages, we found a 4.5μm depth σ comparable to
they were obtained. In each bin of coverage, we fitted a or smaller than that measured at 3.6μm. In particular, at the
Gaussian function to the distribution of aperture fluxes, nominal coverage of the survey, we measure σ = 1.93μJy at
symmetrized with respect to the median to include only 3.6μm and σ(C = 5) = 1.75μJy at 4.5μm. As noted before,
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TheAstrophysicalJournalSupplementSeries,223:1(15pp),2016April Baronchellietal.
Figure4.Skybackgroundnoise,σ,asafunctionofaperturecoverage(seethe
textfordetails).Theexpectedtrendforbackground-dominatednoiseisshown
witharedsolidline,whiletheobservedbest-fitrelationisshownwithagreen
dashed line. We cut the IRAC 3.6μm catalog at a 3σ level, where σ is
estimatedfromtheaveragecoverageofeachsource.
this behavior is due to the effect of latent images being more
pronounced in the 3.6μm channel than in the 4.5μm channel. Figure 5. Top panel: completeness and contamination computed through
numericalsimulations.Thecompletenessleveldropstoapproximately50%at
3.6μmflux∼9μJy.Thecontaminationrate(multipliedbyafactorof100,for
2.3. Survey Completeness and Contamination clarity) reaches a maximum of 1.1% at the 3σ flux. Bottom panel: Flux
accuracyofthesimulatedsources.Theerrorbarsrepresentthe1σrangeofflux
We estimated the survey completeness as a function of the accuracy resulting from the simulations. Source fluxes start to become
3.6μm flux, adding artificial sources to the original IRAC systematicallyunderestimated(bymorethan10%)below∼9μJy.Thevertical
mosaic and extracting them with the same procedure used for dashedlinesshowour3,5,and10σfluxlimits.
the real IRAC map. To create the artificial sources, we
generated a synthetic point-spread function (PSF) using the
median of 76 images of point sources extracted from the Table1
original 3.6μm map. These sources were selected for being CompletenessasaFunctionof3.6μmFlux
isolated (closest counterpart distance >22 0) and with fluxes Completeness Flux3.6μm
near100μJy.Moreover,weexcludedclearlyextendedsources, (%) (μJy)
characterized by KRON_RADIUS × A_IMAGE>10pixels
50 9.0
and sources located at the edge of the map. 75 10.8
We simulated approximately 69,000 artificial sources with 90 14.1
31 different 3.6μm fluxes in the range ∼3–100μJy. For each 95 40.0
of the 31 groups, we simulated an independent IRAC map, 97 107.1
randomly distributing 2233 same-flux artificial sources along 99 224.2
with the real ones. After the extraction, we computed the
detection rate (i.e., the completeness) as the ratio between the
numberofsourcesinsertedinthemapandtherecovered ones.
Figure5,showthattheaccuracyofthefluxmeasurementsisa
This approach allows us to maximize the number of sources
inserted in the maps without artificially increasing the spatial function of the output fluxes. For sources at a 3σ level, the
recovered flux ranges (1σ of the data distribution) from ∼75%
density of the sources, as would happen if we added all the
to ∼10% below the input flux, while for sources at the 10σ
simulated sources at once. The results of this analysis are
level the range is from ∼20% below to ∼30% above the input
presented in Figure 5, where we show that the completeness
drops below 50% at 3.6μm flux of approximately 9.0μJy flux. There is an indication that faint sources (F3.6mm < 5s)
(corresponding to a source flux of approximately 4.7σ). havesystematicallyunderestimated fluxes,althoughthescatter
We also investigate the flux accuracy as a function of the in this range is also larger. The detection rate as a function of
artificial source flux by computing the average difference the 3.6μm flux is summarized in Table 1. These values have
between the flux of the simulated sources and their flux after been used for computing the 3.6μm number counts presented
the extraction. The results, presented in the bottom panel of in Section 4.1.
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TheAstrophysicalJournalSupplementSeries,223:1(15pp),2016April Baronchellietal.
Table2
AvailableAncillaryData
Band Instrument OverlapAreaa Depth NumberofIdentifiedCounterpartsb
(μm) (deg2) All MIPS24 SPIRE MIPS24andSPIRE
3.6 IRAC 7.74 5.80μJy(3σ)c,d 341006 25132 9447 7041
4.5 IRAC 7.26 5.25μJy(3σ)d 320460 23688 9320 6947
24 MIPS 7.66 0.26mJy(50%compl.)e 25132(60) 25132(60) 7041(60) 7041(60)
70 MIPS 7.66 24mJy(50%compl.)e 882 882 692 692
250 SPIRE 6.52 15.6mJy(3σ)f 8743(50) 6666(50) 8743(50) 6666(50)
350 SPIRE 6.52 12.7mJy(3σ)f 9416(60) 7015(60) 9416(60) 7015(60)
500 SPIRE 6.52 18.5mJy(3σ)f 8624(58) 6354(58) 8624(58) 6354(58)
0.65 WFI 1.13 0.53μJy(3σ) 27585 2279 808 680
Notes.
aAreacoveredinboththeIRAC3.6μmbandandinthebandindicatedinthefirstcolumn.
bTheadditionalnumberofMIPS–SPIREsourceswithout3.6μmcounterpartsisindicatedinparenthesis.
cTheIRAC3.6μmcatalogiscutata3σlevel,asdescribedinthetext,keepingintoaccounttheunderlyingcoverageforeachsource.Sourceswithfluxesbelowthe
valuereportedinthetablecanconsequentlybefoundinthecatalog.
dThevalueofσreportedisestimatedforthenominalcoverageofthesurvey(C=5).
eFromClementsetal.(2011).Minimum24μmfluxinthecatalog:0.20mJy.MinimumforMIPSidentifiedsourceswithaSPIREcounterpart:0.31mJy.
f1σvaluesfromOliveretal.(2012).Weincludedinourcatalogonlythesourceswithafluxhigherthen3σinatleastoneoftheSPIREbands.
We estimate the false positive rate (i.e., contamination rate) ∼12deg2intheSEPregionandincludescounterpartsat70μm
applying the same extraction technique and 3σ cut used for the of the 24μm detected sources, and so we limit the analysis to
original 3.6μm map, to the inverted 3.6μm image (pixel fluxes the cross-correlation between IRAC and MIPS 24 and report
multipliedby−1).Weobtainatotalcontaminationrateof0.21%. the 70μm association identified in the original MIPS catalog.
In Figure 5, the contamination (multiplied for a factor of 100) is Clements et al. (2011) estimate that the 24μm catalog is 50%
shownasafunctionofthefluxofthespuriousextractedsources. completeat0.26mJyand80%completeat0.32mJy,whilethe
Spurious sources represent ∼1.1% of all sources at the 3σ level source reliability is 96% at 0.285mJy.
and∼0.25%at5σ.Inordertoobtainanindependentupperlimit InordertoidentifythemostlikelyIRACcounterparttoeach
tothetotalcontaminationrate,weapplytheB–Htest(Benjamini MIPS source, we proceed as follows. For each MIPS 24μm
& Hochberg 1995) under the assumption that the background source, we searched the IRAC catalog for the nearest object
distribution is known and Gaussian. For each flux in the catalog insidearadiusequaltothequadraticsumoftheσofthePSFof
wecanthencomputeitsp-valueunderthenullhypothesisthatit the two instruments (i.e., a search radius of 2 6). In the
was extracted from the background population. The p-value matching process, we identified a small systematic shift24 (of
relative to the 3σ flux threshold adopted for the 3.6μm catalog theorderofΔR.A.=0 099,Δdecl.=0 49)betweenthetwo
(p3σ) corresponds to the value C(p3σ) in the p-values cumulative catalogs. Therefore, we corrected the MIPS positions before
distribution. The expected contamination rate = (1 - a) searching for the nearest IRAC counterpart. We report in the
Np3s C(p3σ)<Np3s C(p3σ),whereNisthenumberofapertures final catalog both the corrected and the original coordinates of
usedinthetestandαistheratiobetweenthenumberofspurious thesourcesineachband.InTable3,wereportthedistanceand
andrealsourcesinthecatalog.Atthenominalcoverageof5,the the average R.A. and decl. shifts of all of the sources matched
upper limit on the contamination rate is then 1.2%. inthecatalog.WhenmultipleIRACsourceswerefoundwithin
the search area (see Figure 7), we associated the closest IRAC
3. ANCILLARY DATA object. This happens for 514 MIPS sources which we flag as
uncertain identifications (“N_IRAC_MIPS” parameter greater
TheSIMESfieldisfullycoveredwithbothSpitzerMIPS(24 than one). All the other potential IRAC counterparts can be
and 70μm), and Herschel SPIRE (250, 350, and 500μm) as found in the catalog. Of all of the MIPS sources (25,132
well as by Akari. In the present paper we report on the MIPS objects), 98.0% have a unique IRAC counterpart within a
andSPIREobservations,whiletheAkaridatawillbediscussed
region of 2 6 radius. MIPS sources without an IRAC
in a forthcoming paper (I. Baronchelli et al. 2016, in
counterpartaregenerallynotincludedinourcatalog.Theonly
preparation). The central square degree is also covered by
exception is represented by 60 visually checked sources in the
optical imaging (see Figure 1). In the following sections we
IRAC covered area with a reliable SPIRE counterpart.
describe how we merged the IRAC-based catalog with the
publicly available MIPS (Clements et al. 2011) and SPIRE
(HerMES, DR2, Roseboom et al. 2010; Oliver et al. 2012; 3.2. SPIRE 250, 350, 500μm
Smith et al. 2012; Wang et al. 2014) catalogs. Section 3.3 TheSIMESfieldwasobservedaspartoftheHerschelMulti-
describes the data reduction, photometry, and matching of the tieredExtragalacticSurvey(HerMES,Oliveretal.2012;Wang
optical data. The main properties of the multiwavelength data et al. 2014). The second data release of the SPIRE XID
are summarized in Table 2. catalogs(DR2,Roseboometal.2010;Smithetal.2012;Wang
et al. 2014) covers approximately 84% of the field, and
3.1. MIPS 24 and 70 μm
The MIPS24μm catalog is described in Clements et al. 24 Weverifiedthattheshiftdidnotdependonthepositioninthelargemosaic,
thus indicating that any distortion in the IRAC mosaic was properly
(2011). The Clements et al. (2011) catalog covers an area of accountedfor.
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TheAstrophysicalJournalSupplementSeries,223:1(15pp),2016April Baronchellietal.
Table3
ParametersUsedintheCounterpartIdentificationProcedurea
MeanDistanceb BarycentrePosition(ΔR.A.,Δdecl.)c
Search
Band Radius BeforeR. AfterR. BeforeR. AfterR. ΔN
( ) ( ) ( ) ( ) ( )
MIPS 2.60 1.01 0.87 0.098,0.49 0.010,0.071 +140(0.6%)
SPIREd 8.04 3.00 3.00 0.019,−0.045 0.0035,−0.0040 −1(0.01%)
2MASS 2.68 0.60 0.59 0.067,−0.067 0.0061,−0.0059 +7(0.005%)
Notes.
aAllthevaluesarecomputedbeforethecorrectionforaverageshift(BeforeR.)andafterthecorrection(AfterR.).
bAveragedistance,inarcseconds,betweentheIRAC3.6μmsourcesandthecorrespondingcounterpartsintheotherbands.
cAveragedifference(ΔR.A.,Δdecl.)betweentheIRAC3.6μmsourcesandthecorrespondingcounterpartsintheotherbands.
dDistancesrefertotheMIPS24positions.
includes all sources identified at the 1σ level at 250, 350, or
500μm. Here, we keep only those sources with fluxes above
3σ in at least one SPIRE band.
ThelargesizeoftheSPIREPSF(18 0at250μm)prevents
us from directly cross-correlating the SPIRE and IRAC
catalogs. Instead, we exploited the MIPS24μm detections as
a “bridge” between the two wavelengths, i.e., we searched the
SPIRE counterparts given the MIPS 24 prior position. We
performed a direct SPIRE–IRAC correlation only when a
SPIRE source didnot havea MIPS counterpart. As we didfor
the MIPS–IRAC correlation, before correlating the SPIRE and
MIPScounterparts,wecorrectedtheSPIREcoordinatesforthe
smallaverageoffsetbetweenMIPSandSPIREpositionsusing
the original MIPS coordinates as reference. In our catalog, we
reportboththeoriginalSPIRE(R.A.,decl.)coordinatesaswell
as the coordinates corrected to the average SPIRE–MIPS and
then MIPS–IRAC shifts.
Given the MIPS positions, we searched the SPIRE counter-
parts inside a radius of 8 04 (quadratic sum of the PSF’s σ of
thetwoinstruments).WhenasingleSPIREsourceisassociated
with two (or more) different MIPS sources, we consider both
associations.BoththeSPIREandMIPSfluxesareproportional
Figure6.MIPS-undetectedSPIREsourcesarelikelylocatedathigherredshift
tothetotalIRluminosity(LIR,e.g.,Chary&Elbaz2001;Elbaz than MIPS-detected sources. Normalized 3.6μm fluxes (top panel) and
et al. 2011). This is due to the same thermal origin of the [3.6μm]–[4.5μm]ABcolordistributions(bottompanel)forSPIREsourcesin
radiation emitted in these bands. Given this assumption, when our catalog. The SPIRE sources with and without MIPS counterparts are
wefindmultipleMIPScounterpartsforasingleSPIREsource, indicated in green and red, respectively. The median of each distribution is
representedwithadashedlineinthesamecolorcode.
theoriginalfluxesinthethreeSPIREbandsaredividedamong
the MIPS counterparts proportionally totheir 24μmflux. This
multipleassociationinvolves429MIPSsources,flaggedinour heated by optical-UV radiation produced by young stars inside
catalog through a N_MIPS_SPIRE parameter greater than 1. starformingregionsorbyaccretiondisksofactivegalacticnuclei
SPIRE–MIPS associations outside the IRAC covered area are (AGNs).Atincreasingredshifts,theSPIREbandssamplespectral
not included in our catalog. Using the MIPS 24 prior position, regionsclosertotheredshiftedpeak.Instead,beinglocatedonthe
we found 7034 SPIRE counterparts for our IRAC sources. opposite side of the thermal emission bump, the MIPS 24μm
FortheremainingSPIREsourceswithoutaMIPScounterpart,
bandsamplesaspectralregionwhosetheemittedluminositytends
we searched for a direct IRAC–SPIRE association. We found
tobelowerathigherredshifts.Thecombinationoftheseeffectsis
2413 SPIRE sources with associated IRAC counterparts. These
responsible for the lower MIPS detection rate among the SPIRE
are flagged in the catalog with N_IRAC_SPIRE > 0 and
detected sources at higher redshifts. This is demonstrated in
N_MIPS_SPIRE0.TheMIPS-undetectedSPIREsourceshave Figure 6 where we show the distributions of 3.6μm flux and
24μm flux below the detection threshold in this band. This is
[3.6μm]–[4.5μm]colors(i.e.,2.5log(F F ))forSPIRE
because for SPIRE detected sources, the detection rate at 24μm 4.5mm 3.6mm
sourceswithandwithoutMIPScounterpart.ThetwoIRACbands
stronglydependsonthesourceredshift.Thisisduetothetypical
sample the stellar 1.6μm bump (Simpson & Eisenhardt 1999;
shapeoftheSEDoffar-IRdetectedsources25,usuallypresenting
Sawicki 2002) and the IRAC color is expected to change with
a bump of emission centered at ldust ~ 100μm(Kirkpatrick
et al. 2012). This bump is due to thRe.Fth.ermal emission of dust redshift as the peak moves through the two filters. The color
distributions in Figure 6 are clearly different, indicating that
25 SomeexamplesoftypicalSEDsfordifferenttypesofgalaxiescanbefound MIPS-undetected SPIRE sources are more commonly located at
in,e.g.,Pollettaetal.(2007). higher redshifts than the MIPS-detected SPIRE sources. This is
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TheAstrophysicalJournalSupplementSeries,223:1(15pp),2016April Baronchellietal.
Figure8.Correlationreliabilityindicator—P—computedfortheSPIRE–MIPS
Figure 7. Fraction of sources with one or multiple counterparts inside the (P1), MIPS–IRAC (P2), and SPIRE–IRAC (P3) positional correlations. The
searchradiusfortheSPIRE–MIPS,MIPS–IRAC,andSPIRE–IRACpositional latter was computed for all SPIRE sources in our catalog, even when the
correlations.Inthetoppanel,weshowthedistributionofthenumberofIRAC correlation with IRAC is found through the MIPS position. The black filled
counterparts inside the SPIRE–IRAC search aperture for the full sample of distributioninthetoppanelshowsSPIREsourceswithoutaMIPScounterpart.
SPIRE sources (hatched histogram). The black filled histogram shows the TheredfilleddistributioniscomputedforSPIREsourceswithmultipleIRAC
distributionofthenumberofIRACcounterpartsforSPIREsourceswithouta counterpartsinsidetheIRAC–SPIREsearchradius.
MIPScounterpart.
also supported by the lower median 3.6μm flux of the MIPS- only real counterparts, but in this case, only ∼48% of our
undetected SPIRE sources (upper panel of Figure 6). SPIRE source have a single IRAC counterpart inside the
searhingradius.Inordertoassessthereliabilityofthematched
SPIRE sources lacking an IRAC counterpart are generally
counterparts,wedevelopaparameter,P,thataccountsforboth
notincludedinourcatalog.However,weincludeinourcatalog
thecounterpartdistanceandthenumberofsourcesfoundinthe
the 60 SPIRE sources with a reliable MIPS counterpart but
search radii.
without any IRAC counterpart inside a distance corresponding
to two times the IRAC–MIPS search radius. We visually Asmentionedearlier,wedefinethesearchareabytheradius
checked all of the sources in the IRAC 3.6μm image in order rs equal to the the quadratic sum of the PSF’s σ of the two
to exclude missed detections due to the presence of nearby instruments involved (e.g., r = s2 + s2 , for IRAC–
s IRAC MIPS
bright sources or border effects enhancing the noise, and MIPS correlation). We consider a normalized 2D Gaussian
consequently, the detection threshold. function with σ = r; the counterpart distance d is always
s i
The catalog includes a total of 9447 SPIRE sources with a smaller then r. We then compute the quantity A as the
s i
SPIRE flux (in at least one band) above 3σ. The reliability of probabilityofthegalaxybeingatadistancegreaterthand,for
i
their association with the IRAC counterpart is discussed inthe thegivenGaussianfunction.Forasinglecounterpart,centered
next section. on the coordinates of the starting objects, A = 1; in general,
i
A decreases as the distance of the counterpart increases. We
i
then account for the presence of multiple counterparts (at
3.2.1.Counterpart Reliability
different d), by defining the parameter P as follows:
TheprobabilityofhavingtherightMIPS(IRAC)counterpart i
associated with theSPIRE(MIPS) sourcedepends onboththe A
P = A 1 , (1)
numberofgalaxiesfoundwithinthesearcharea,aswellasthe 1åA
distance to the identified counterpart(s). In Figure 7 we show i i
the fraction of MIPS and SPIRE sources with one or more
IRACorMIPScounterpartinsidethesearchradius.Only∼2% where A1 is defined as Ai corresponding to the closest
of our MIPS sources have more than one single IRAC counterpart. For multiple counterparts the factor A1 åAi is
counterpart inside the search radius. In all these cases we smaller than one, and decreases with the number of
consider the closest IRAC source as the only real counterpart. counterparts.
Instead, ∼6% of our SPIRE–MIPS associations are made We computed the values of P for the SPIRE–MIPS (P1),
dividing the SPIRE fluxes among the multiple MIPS counter- MIPS–IRAC (P2), and SPIRE–IRAC (P3) correlations. The
parts, as explained in Section 3.2. In the direct SPIRE–IRAC distributions of the P values are represented in Figure 8. The
associations we considered the closest IRAC sources as the directSPIRE–IRACcorrelationisstudiedforthewholeSPIRE
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TheAstrophysicalJournalSupplementSeries,223:1(15pp),2016April Baronchellietal.
sample in our catalog, considering also the sources for which geometrical association, their reliability can be estimated as:
we found a correlation through the MIPS position.
TheMIPS–IRACandSPIRE–MIPSassociationreliabilityis P + P
Preal(cat) = 2 4 ~ 0.92. (6)
usuallyhigh,asdemonstratedbythedistributionofP1andP2. det P + P + P
1 2 4
As a consequence, the association of the SPIRE sources with
the IRAC counterparts through the MIPS position is still In reality, if a purely geometrical IRAC counterpart is
reliable,eveniftheP3distributionisnotasnarrowasthoseof present, it can be closer to the SPIRE position than the real
P1 and P2. counterpart. This is more probable when the IRAC–SPIRE
Using a Monte Carlo simulation, we assessed the reliability distance is higher (and consequently the P3 parameter lower).
of those associations in which multiple MIPS counterparts are Moreover, as stated before, about 6%–8% of the SPIRE
associated with individual SPIRE sources (see Section 3.2). sources have more than a single real counterpart, further
Thesamesimulationalsoallowsustodeterminethereliability reducing the reliability for this sample. For all SPIRE sources
of the direct SPIRE–IRAC associations. The simulation is in our catalog we report the number of potential IRAC
performedbyfirstrandomlyshiftingthepositionoftheSPIRE counterparts in the search radius (N_IRAC_SPIRE) and the
sources to 10, 20, 30, and 50 times the search radius from the parameter P3.
original position and then looking for potential IRAC counter-
parts. We found a detection rate of approximately 49.7%
3.3. Optical R Band
± 0.1%. c
Inourcatalog,amongtheSPIREcounterpartsfoundthrough A central area of approximately one square degree was
a MIPS prior position, the fraction of SPIRE sources having observedattheMPG/ESO2.2mtelescopeatLaSillawiththe
multiple IRAC potential associations is 57.6%. SPIRE sources Wide Field Imager (WFI) during 2010 October (P.I.: T.
withMIPScounterpartsaregenerallyassociatedwithhighflux Takeuchi). The eight CCDs of the WFI camera cover a total
IRAC counterparts: only 4.0% of them have an IRAC flux area on the sky of34¢ ´ 33¢, with a pixel scale of 0 24. Four
below the 90% completeness limit (see Table 1). Moreover, pointings with the R broadband filter (λ = 6517.25Å) were
c c
multipleIRACcounterpartsinaMIPS–IRACsearchradiusare obtained,coveringatotalareaof1.13deg2.Eachpointingwas
very rare. This means that purely geometrical MIPS–IRAC observedwithmultipleexposuresditheredtooptimallyremove
associations are highly improbable. Therefore, we can safely the gaps between different CCDs and other CCD defects. The
assume that for almost all our SPIRE counterparts found total exposure time varied between 2.2 and 1hr.
through MIPS prior positions, there is a detected IRAC real ThedatawerereducedwithstandardIRAFroutinesincluded
counterpart. Given these assumptions and the results of our in the NOAO mosaic software MSCRED. A Super-sky Flat-
simulation, we expect that 49.7% of the SPIRE sources, aside field correction was applied by dividing all of the science
from the real IRAC counterpart, have an additional purely framesfortheaverageofthenon-alignedandsource-subtracted
geometrical association. Since we measure a real multiple scienceexposures. The finalR mosaic was created combining
c
association rate of 57.6%, the additional 7.9% of these SPIRE the images on the four pointings. In the final mosaic, the
sources must have real multiple IRAC components. Indeed, in FWHM of the PSF was 1 0.
our catalog, ∼6.1% of the SPIRE sample have multiple MIPS Sources were extracted from the final mosaic using the
counterparts,andallofthemhaveoneIRACcounterpartinside SExtractor software. We considered only sources with five
the MIPS–IRAC search radius. The ∼2% difference confirms connected pixels above a threshold of 1.0σ of the local
the reliability of the multiple SPIRE–MIPS associations background For each detected object, we recorded in the
discussed in Section 3.2. catalog the total AUTO flux. The photometric calibration of
As can be observed inthe toppanel of Figure 6, the3.6μm these data is obtained through the comparison with Bruzual &
fluxfortheSPIREsourcesisgenerallylowerwhentheyarenot Charlot (2003, BC03)template SEDs fitted to a large set of
detectedat24μm.AsexplainedinSection3.2,thelowerIRAC opticaldata.WeusedasetofmeasuresobtainedintheSIMES
flux of these MIPS-undetected sources can be explained with field for a selected sample of sources, in 13 different filters
the higher median redshift of the these sources. We find that coveringthespectralrangebetweentheuandtheIRAC4.5μm
∼24% of this sample are below the 90% completeness level, bands(I.Baronchelli etal.2016,inpreparation).Knowing the
resulting in an overall completeness of ∼85%. Therefore, the spectroscopic redshift of the selected sources (Sedgwick
probability of detecting the real counterpart inside the search et al. 2011), we used a χ2 minimization technique (i.e.,
radius is Preal = 0.85 while, from our simulation, the hyperzmass,Bolzonellaetal.2000)tofindthebest-fittingSED
det
probability to find a purely geometrical IRAC counterpart is amongtheBC03templateSEDs.Aftercomparingtheextracted
Pgeom = 0.497. We define the following products: R fluxwiththeexpectedfluxobtainedfromtheconvolutionof
det c
the WFI-R filter response with the best-fitting SED, we used
P1= Pdgeetom(1 - Pdreetal), (2) the averagec difference to calibrate the extracted Rc fluxes.
P = PgeomPreal, (3) Wecomputedthedepthoftheopticalmosaicasfollows.We
2 det det measured the flux inside randomly distributed 1 9 diameter
P3= (1 - Pdgeetom)(1 - Pdreetal), (4) aperturesandwefittedaGaussianfunctiontothesymmetrized
distributionoffluxvalues.The3σfluxlimitoftheR imageis
P4 = (1 - Pdgeetom)Pdreetal. (5) 0.53μJy. c
Each SPIRE source can either have a possible counterpart InordertocombinetheopticalandIRdata,wesearchedthe
(with probability P = P + P + P) or not (P = P), IRAC catalog for the closest counterpart to each R-detected
det 1 2 4 no‐det 3
withP + P + P + P = 1.AllSPIREsourcesinourcatalog source using a search radius of 0 82. The precise technique
1 2 3 4
haveoneIRACassociation.AssumingthattheSPIREposition adopted is described in I. Baronchelli et al. (2016, in
isalwaysclosertotherealIRACcounterpartthantothenearest preparation). We found an optical counterpart for ∼55% of
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TheAstrophysicalJournalSupplementSeries,223:1(15pp),2016April Baronchellietal.
Figure 9. IRAC 3.6, MIPS 24μm, and R fluxes for a library of templates
c
includingbothstars(red,usingthestellarmodelsofKurucz1993)andgalaxies
(green,intherange0<z<2.5andnormalizedtoνLnh2=1010Leat3.6μm).
The selection thresholds used are represented with dashed black lines. The
blackboxesrepresenttheareascoveredbytheplotsofFigure10.
theIRAC-detectedsourcesinourcatalogwhicharecoveredin
the R band.
c
Figure 10. Galaxy/star separation. Same as Figure 9, but plotting the
measurementfortheobjectsdetectedintheSIMESfield.Ineachpanel,green
3.4. Galaxy/Star Separation pointsshowsourcesthathavebeenclassifiedasgalaxiesbyoneofourcriteria
(seethetextfordetails).
We perform the galaxy/star separation only in the central
square degree area covered by the optical data. Our separation
asagalaxyanyobjectdetectedinoneoftheSPIREbands.This
criteria, described in detail below, are based on a combination assumption is confirmed by a visual inspection of the SPIRE
of diagnostics using optical, 3.6 and 24μm fluxes, as well as sourcesontheIRACimageswherethebright3.6μmsaturated
the surface brightness profile of each source from the optical
stars are not SPIRE detected.
data. Our selection criteria were calibrated using the stellar
Then, following our diagnostics, we identify stars in the
spectral models of Kurucz (1993) and a representative set of 3.6μmversusMIPS24plane(toppanelsofFigures9and10).
galaxy SED templates from Polletta et al. (2007), including
All sources with log(F [Jy]) < log(F ) - 0.7 are classi-
24 3.6mm
ellipticals, spiral galaxies, starburst galaxies, and QSO fied as stars. Because of the bright MIPS 24 flux limit
templates. (logF [Jy]~−3.6),thisselectionmissesfaintIRAC-detected
We considered galaxy models in the range 0 < z < 2.5 and 24
stars. We thus implement two additional constraints, based on
with νLnh2 = 1010Le at 3.6μm, close to the characteristic L* the R −3.6μm color (bottom panel of Figure 9 and middle
luminosity (e.g., Babbedge et al. 2006; Franceschini et al. panelcof Figures 10) and the SExtractor CLASS_STAR26
2006). We used the Kurucz (1993) stellar models with parameter measured in the 3.6μm image (bottom panel in
abundances relative to solar ranging from log(Z) = 1.0 to Figure 10). The SPIRE and 24μm undetected sources are
log(Z) = -5.0. In Figure 9, the differences between the areas classifiedasstarswhenlog(F [Jy]) > log(F ) + 0.1,and
occupied by the solar metallicity models and by the models Rc 3.6mm
as galaxies when log(F [Jy]) < log(F ) - 1.1. Between
withallthepossiblemetallicitiesareshown.Foreachmodeled Rc 3.6mm
these two limits, or when no SPIRE, MIPS, or R counterparts
starorgalaxy,wecomputedtheexpectedfluxesinthe3.6μm, are found, we rely on the combination of the 3.6cμm flux and
24μm, and R bands. In Figure 9, galaxy tracks are shown in
c the CLASS_STAR parameter to identify stars in our sample.
green (different sizes correspond to different redshifts).
The reliability of the SExtractor CLASS_STAR parameter
Regions in the diagnostic diagrams occupied by stars are worsens at the faintest and brightest IRAC fluxes. At
shown as the shaded red band. low fluxes (i.e., F  10-4Jy), the shape of a galaxy
The bulk of the stellar emission for a galaxy is located at λ 3.6mm
looks similar to that of a point-like source, while the
∼1.6μm (Simpson & Eisenhardt 1999; Sawicki 2002). Stars PSF wings of the brightest objects (i.e., F  10-2.0Jy)
are generally fainter at longer wavelengths, especially at the 3.6mm
can be incorrectly interpreted as due to an extended
SPIRE wavelengths, wherethegalaxyspectra isdominatedby
the dust thermal emission. For this reason, among the SPIRE
26 CLASS_STAR = 0 for galaxies, = 1 for stars. This SExtractor output
sources, the probability of detecting a star is negligible if parameterquantifiesthesimilaritiesbetweenasourcesurfacebrightnessprofile
compared to that of detecting a galaxy. Therefore, we classify andtheprofileofapoint-likesource.
10