The ALHAMBRA survey

18 Pages·2013·10.83 MB·English

MNRAS435,3444–3461(2013) doi:10.1093/mnras/stt1538
AdvanceAccesspublication2013September13
The ALHAMBRA survey: reliable morphological catalogue of 22 051
early- and late-type galaxies
M. Povic´,1‹ M. Huertas-Company,2,3 J. A. L. Aguerri,4 I. Ma´rquez,1 J. Masegosa,1
C. Husillos,1 A. Molino,1 D. Cristo´bal-Hornillos,5 J. Perea,1 N. Ben´ıtez,1
A. del Olmo,1 A. Ferna´ndez-Soto,6,7 Y. Jime´nez-Teja,1 M. Moles,1,5 E. Alfaro,1
T. Aparicio-Villegas,1,8 B. Ascaso,1 T. Broadhurst,9,10 J. Cabrera-Can˜o,11
F. J. Castander,12 J. Cepa,4,13 M. Fernandez Lorenzo,1 M. Cervin˜o,1,4
R. M. Gonza´lez Delgado,1 L. Infante,14 C. Lo´pez-Sanjuan,5 V. J. Mart´ınez,7,15
I. Matute,1 I. Oteo,4,11 A. M. Pe´rez-Garc´ıa,4,13,16 F. Prada1 and J. M. Quintana1
1InstitutodeAstrof´ısicadeAndaluc´ıa(IAA-CSIC),E-18008,Granada,Spain
2GEPI,Paris-MeudonObservatory,F-75014Meudon,France
3UniversityofParis,F-75205Paris,France
4InstitutodeAstrof´ısicadeCanarias(IAC),E-38200LaLaguna,Tenerife,Spain
5CentrodeEstudiosdeF´ısicadelCosmosdeArago´n(CEFCA),E-44001Teruel,Spain
6InstitutodeF´ısicadeCantabria(CSIC-UC),E-39005Santander,Spain
7UnidadAsociadaObservatoriAstrono`mic(IFCA–UV),Valencia,Spain
8Observato´rioNacional-MCT,RuaJosCristino,77.CEP20921-400RiodeJaneiro-RJ,Brazil
9DepartmentofTheoreticalPhysics,UniversityofBasqueCountry,UPV/EHU,POBox644,E-48080Bilbao,Spain
10IKERBASQUE,BasqueFoundationforScience,AlamedaUrquijo36-5,E-48008Bilbao,Spain
11FacultaddeF´ısica,DepartamentodeF´ısicaAto´mica,MolecularyNuclear,UniversidaddeSevilla,E-41012Sevilla,Spain
12InstitutdeCie`nciesdel’Espai,IEEC/CSIC,E-08193Barcelona,Spain
13DepartamentodeAstrof´ısica,FacultaddeF´ısica,UniversidaddelaLaguna,E-38200LaLaguna,Spain
14DepartamentodeAstronom´ıa,PontificiaUniversidadCato´lica,SantiagodeChile,Chile
15ObservatoriAstrono`micdelaUniversitatdeVale`ncia,Valencia,Spain
16Asociac´ıonASPID,ApartadodeCorreos412,E-38200LaLaguna,Tenerife,Spain
Accepted2013August13.Received2013August5;inoriginalform2013April9
ABSTRACT
Advanced Large Homogeneous Area Medium Band Redshift Astronomical (ALHAMBRA)
isphotometricsurveydesignedtotracethecosmicevolutionandcosmicvariance.Itcoversa
largeareaof∼4deg2 ineightfields,wheresevenfieldsoverlapwithothersurveys,allowing
ustohavecomplementarydatainotherwavelengths.Allobservationswerecarriedoutin20
continuous,mediumband(30nmwidth)opticaland3near-infrared(JHK)bands,providing
theprecisemeasurementsofphotometricredshifts.Inaddition,morphologicalclassification
ofgalaxiesiscrucialforanykindofgalaxyformationandcosmicevolutionstudies,providing
the information about star formation histories, their environment and interactions, internal
perturbations, etc. We present a morphological classification of >40 000 galaxies in the
ALHAMBRA survey. We associate to every galaxy a probability to be early type using the
automatedBayesiancodeGALSVM.DespiteofthespatialresolutionoftheALHAMBRAimages
(∼1 arcsec), for 22 051 galaxies, we obtained the contamination by other type of less than
10percent. Of those, 1640 and 10 322 galaxies are classified as early- (down to redshifts
∼0.5)andlate-type(downtoredshifts∼1.0),respectively,withmagnitudesF613W≤22.0.
Inaddition,formagnituderange22.0<F613W≤23.0,weclassifiedother10089late-type
galaxieswithredshifts≤1.3.Weshowthattheclassifiedobjectspopulatetheexpectedregions
(cid:2)
E-mail:[email protected]
(cid:4)C 2013TheAuthors
PublishedbyOxfordUniversityPressonbehalfoftheRoyalAstronomicalSociety
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ALHAMBRA:morphologicalclassification 3445
inthecolour–massandcolour–magnitudeplanes.Thepresenteddatasetisespeciallyattractive
giventhehomogeneousmultiwavelengthcoverageavailableintheALHAMBRAfields,and
isintendedtobeusedinavarietyofscientificapplications.Thelow-contaminationcatalogue
(<10percent)ismadepubliclyavailablewiththispaper.
Keywords: surveys–galaxies:fundamentalparameters–galaxies:statistics.
Morgan & Mayall 1957; Baldwin, Phillips & Terlevich 1981;
1 INTRODUCTION
Folkes,Lahav&Maddox1996;Sa´nchez-Almeidaetal.2010),or
Oneofthefirststepsinanyresearchworkistogroupobjectswith lightdistribution(e.g.Abrahametal.1994;Conseliceetal.2000;
common properties (e.g. shape, weight, colour, etc.). This taxon- Abrahametal.1996;Abraham,vandenBergh&Nair2003;Lotz,
omyisapowerfultoolinordertounderstandthephysicsbehindthe Primack&Madau2004;Scarlataetal.2007).Currentworksareled
formationandevolutionofthestudiedobjects.Probablythemost to more complex fitting models, using orthonormal mathematical
popular classification of galaxies is based on the shapes or mor- basestodecomposethegalaxies,andthencorrelatingthephysical
phologies(startedwithReynolds1920;Hubble1926,1936).This properties of the objects with the coefficients of the decomposi-
first order classification has survived over time since the differ- tionbymeansofaprincipalcomponentanalysis.Thisisthecase
entmorphologicalclassesofgalaxieshavealsodifferentphysical of the shapelets (Kelly & McKay 2004, 2005; Andrae, Jahnke &
properties and probably different evolutionary tracks. In general, Melchior 2011a), or future works with the sersiclets (Ngan
galaxies can be divided into two main classes: early-types (here- et al. 2009; Andrae, Melchior & Jahnke 2011b), and the CHEFs
afterETs)andlate-types(hereafterLTs).ETsincludeellipticaland (Jime´nez-Teja&Ben´ıtez2012).Themainadvantageoftheselatter
lenticulargalaxies,whileLTsincludespiralsandirregulargalaxies. methodswithregardtotheformeristheirflexibilityandreliabilityto
ETsappeartobeafamilyofobjectsshowingoldstellarpopulations, reproduceeveryfeatureinthegalaxies,andthentoefficientlymodel
spheroidal-like dynamical properties and a small fraction of cold every kind of morphology, including irregular objects. Moreover,
gaswhereasLTsaremoregas-richobjects,presentyoungerstellar they do not need any a prioriknowledge and they donot impose
populations,andaremainlyrotationsupported. anyprofiletofitthegalaxies.However,thesemathematicalmodels
Visual inspection is the traditional way to classify galaxies. arecomputationallymoreexpensiveandtheparametersofthefinal
Bydefinition,itissubjectiveand notreproducible,butforbright decompositiondonotprovideanyphysicalinformation.ALHdata
and extended objects there is a general good agreement between willbeusedforfurtherdevelopmentoftheCHEFsmethod(Jime´nez
differentobservers.Theyarehowevertimeconsuming.Inthepast, etal.,inpreparation).
galaxysamplescontainedfromdozensuptohundredsofgalaxies GALSVM(Huertas-Companyetal.2008)providesageneralization
whilepresentgalaxysurveyshaveuptomillionsofgalaxies.This of the non-parametric classifications by using an unlimited num-
makesimpossibletogivedetailedmorphologicalclassificationsun- ber of dimensions and providing a probabilistic output following
lessalargeamountofclassifiersareinvolved(seetheGalaxyZoo a Bayesian approach (see also Fasano et al. 2012 for a similar
project;Lintottetal.2008). approach). Thealgorithmhasbeenshowntobeespeciallyuseful
Overthepastfewyears,differentautomatedmethodsofmorpho- whendealingwithlow-resolutionandhigh-redshiftdata(Huertas-
logicalclassificationofgalaxieshavebeendeveloped.Automated Company et al. 2009) and has been successfully applied to sev-
classifications resolve the two main problems raised above. They eral large samples at low and high redshift (Huertas-Company
provideindeedreproducibleinformation,andtheerrorscanbefully et al. 2009, 2011), including the Cosmological Evolution Survey
understoodbyusingextensivesimulations(e.g.Trujilloetal.2001; (COSMOS;1 Scoville et al. 2007) and Sloan Digital Sky Survey
Simard et al. 2002, 2011). In addition, modern galaxy classifica- (SDSS;2Castander1998)samples.
tionalgorithmsareabletoclassifylargesamplesofgalaxiesina In this paper, we present the morphological classification of a
reasonableamounttime. largesampleofgalaxiesfromtheAdvancedLargeHomogeneous
Weusuallydistinguishbetweenthreebroadgroupsofautomated AreaMediumBandRedshiftAstronomical,ALHAMBRAsurvey
galaxyclassifications:parametricbasedongalaxyphysicalparam- (hereafter ALH; see Moles et al. 2008), located at different red-
eters, non-parametric and parametric based on mathematical pa- shifts.TakingintoaccounttheresolutionofALHdata(∼1arcsec)
rameters.Parametricclassificationsuseparametricmodelsinorder andalltheadvantagesofGALSVMcodementionedabove,weused
toreproducesomegalaxymeasurements.Oneofthemostpopular thismethodforourmorphologicalclassification.TheALHsurvey
parametricmethodsbasedongalaxyphysicalparametersclassifies imaged ∼4 deg2 of the sky through 23 optical and near-infrared
galaxies according to some properties of their structural parame- (NIR) filters. The large number of filters provide accurate photo-
tersobtainedbyfittingthesurfacebrightness(e.g.deVaucouleurs metricredshiftsbyfittingthespectralenergydistributions(hereafter
1948;Se´rsic1963;Prietoetal.1997,2001;Pengetal.2002,2010; SEDs)ofthegalaxiesforaboutonemillionsources.Thisgalaxy
Simard et al. 2002, 2011; Aguerri et al. 2004, 2005; de Souza, sampleisidealinordertostudyevolutionarypropertiesofgalaxies
Gadotti & dos Anjos 2004; Me´ndez-Abreu et al. 2008). On the inthelast8Gyr(Molesetal.2008;Cristo´bal-Hornillosetal.2009;
otherhand,non-parametricgalaxyclassificationsarebasedonthe Matuteetal.2012;Oteoetal.2013a,b).
measurements of a set of galaxy parameters that correlate with The ALH survey, data and sample selection are introduced in
the morphological types. These methods have the advantage that Section2.Themethodologyusedformorphologicalclassification
theyassumenon-parametricmodelsandcanhenceclassifyregular
and irregular objects. Several galaxy parameters have been used
todiscriminatebetweendifferentmorphologicaltypes,i.e.colours 1http://cosmos.astro.caltech.edu/
(e.g.Stratevaetal.2001),spectralproperties(e.g.Humason1931; 2http://www.sdss.org/
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3446 M.Povic´ etal.
Table1. ALHobservationsusedinthispaper:ALHfieldsandtheircentralcoordinates,numberofobjectsandcoveredareasintheF613W
banddowntomagnitudes23.0,rangeofseeingofindividualobservationsusedtocreatethefinalones,andtheaveragedseeing.
Field RA(J2000) Dec.(J2000) Num.ofobj.inF613Wband Area Min–maxseeing Averagedseeing
(hms) (◦(cid:6)(cid:6)(cid:6)) (atmag≤23.0) (deg2) (arcsec) (arcsec)
ALH-2/DEEP2 022832.0 +004700 14322 0.5 0.86–1.40 1.04
ALH-3/SDSS 091620.0 [phone] 0.5 0.70–1.18 0.89
ALH-4/COSMOS 100028.6 +021221 7104 0.25 1.06–1.32 1.17
ALH-5/HDF-N 123500.0 [phone] 0.25 0.95–1.40 1.23
ALH-6/GROTH 141638.0 [phone] 0.5 0.81–1.30 1.11
ALH-7/ELAIS-N1 161210.0 [phone] 0.5 0.84–1.40 1.04
ALH-8/SDSS 234550.0 [phone] 0.5 0.72–1.40 0.91
Figure1. Exampleoftwogalaxiesobservedwith20opticaland3NIRALHfilters.
is described in Section 3, and the main results are discussed in from340to970nm,withnon-overlappingandequal30nmwidth
Sections4–6.Finally,AppendixAdescribesthecontentofthefirst medium bands (Aparicio-Villegas et al. 2010). This set of opti-
publishedmorphologicalcatalogueofgalaxiesintheALHsurvey. calfilterswasspeciallydesignedfortheALHsurveytoachievea
Weassumedthefollowingcosmologicalparametersthroughout goodaccuracyofphotometricredshiftsof∼δz/(1+z)=0.015for
thepaper:(cid:3)(cid:4)=0.7,(cid:3)M=0.3andH0=70kms−1Mpc−1.Unless galaxiesbrighterthanF814W≤24.5,threetimesbetterthantheone
otherwisespecified,allmagnitudesaregivenintheABsystem(Oke achievedwhenusingthe4–5filtersystems(formoreinformation,
&Gunn1983). seeBen´ıtezetal.2009).Fig.1showstheexampleoftwogalaxies
observedinallALHbands.
OpticaldatawereobservedwithLargeAreaImagerforCalarAlto
2 DATA (LAICA4),withthetotalexposuretimeof100000sperpointing.
Ontheotherhand,OMEGA20005instrumentwasusedintheNIR,
TheALHobservationswerecarriedoutattheCalarAltoGerman-
SpanishAstronomicalCenter(CAHA3),undertheSpanishguaran- withthetotalexposuretimeof60000sperpointing.Datareduction
teedtimeof110nights.EightfieldswereobservedintheNorthern was carried out using the standard set of IRAF6 packages. Table 1
givesthesummaryofALHobservationsusedinthiswork.
hemisphere sky, having a seeing lower than 1.6 arcsec (ranging
mainlybetween0.8and1.2arcsec).Allfields(exceptALH-1)have
themultiwavelengthinformationavailablefromother(deep)extra-
galactic surveys (see Moles et al. 2008 and Table 1). Each ALH
4http://www.caha.es/CAHA/Instruments/LAICA/index.html
detectionwasobservedin23bands,with20opticalandthreestan-
5http://w3.caha.es/CAHA/Instruments/O2000/index2.html
dardJHKNIRfilters.Opticalrangeiscoveredinacontinuousway
6IRAF is distributed by the National Optical Astronomy Observatory
(NOAO),whichisoperatedbytheAssociationofUniversitiesforResearch
inAstronomy(AURA)undercooperativeagreementwiththeNationalSci-
3http://www.caha.es/ enceFoundation(NSF).
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ALHAMBRA:morphologicalclassification 3447
Source detection was performed by means of the SEXTRACTOR like,andequalto0forextendedsources.Unclassifiedsourceshave
(v.2.8.6)code(Bertin&Arnouts1996).Both,detectionofsources GEOM_CLASS_STAR=99.TakingintoaccounttheALHreso-
andcreationofphotometriccatalogues,werecarriedoutinall23 lution,itwasconfirmedthatthismethodworkswelldowntomag-
bands,whereeachcataloguecontainsmorethan180photometric, nitudesr=23.0,whichisingoodagreementwithourmagnitude-
astrometricandbasicmorphologicalparameters.Morethan670000 selectioncriteria(seebelow).
sourcesweredetectedinallALHsurveys,withaphotometriccom- To select the extended sources, we used the
pletenessofr∼25.0(correspondstoSDSSrbandconstructedfrom GEOM_CLASS_STAR=0.Withthiscriteria,usingthecompari-
fiveindividualALHbands).Alladditionalinformationrespectto sons with the Hubble Space Telescope (HST) data from the
thesourcedetectionandcreationofphotometriccataloguescanbe COSMOS survey, we estimated to have in the selected sample
found in Husillos et al. (in preparation), while the catalogues are contaminationofpoint-likesourceslowerthan1percentdownto
publiclyavailableviathesurveywebpage.7 magnitudes22.0intheF613Wband,andof5–7percentbetween
For photometric redshift estimations (hereafter photo-z), the magnitudes22.0and23.0.
BayesianPhotometricRedshiftcodewasused(BPZ;Ben´ıtez2000; (ii) Photo-zselection.Weusedtwocriteriatoselectthesources
Ben´ıtezetal.2004).BPZwasrunonaseparatedpointspreadfunction with good photo-z measurements. First, we only selected those
(PSF)correctedphotometryandanewlibraryoftemplates(Ben´ıtez sources detected in all ALH filters. And secondly, we selected
etal.,inpreparation)wasimplemented,composedby11SEDs(4el- sources with BPZ_ODDS10 parameter above 0.2. With these two
liptical,1lenticular,1Sbc,1Scdand4starburstgalaxies),originally criteria,weexpecttohavelessthan3percentofoutliers(Molino
drawnfromPEGASE(Fioc&Rocca-Volmerange1997),butthenop- etal.2013).Wecheckedthatrequiringtheobjectstobedetected
timizedusingtheFIREWORKSphotometryandspectroscopicredshifts inallALHbandsintroducesonlyasmallselectionbiasaffecting
fromWuytsetal.(2008).Basedonasampleof∼7000galaxieswith thesampleofredgalaxiesatz>0.4andmagnitudes<22inthe
known spectroscopic redshifts, drawn from different surveys (see F613Wband,arangeinwhichourmethodisnotabletoefficiently
Table1),thefinalperformanceofresultingphoto-zswasevaluated. selectETs(seeSection4).
Theexpectedaccuracyforgalaxieswithmagnitudes<23.0inthe (iii) Magnitude selection. We selected only objects with mag-
constructedF814Wnmbandis∼δz/(1+z)=0.011withasmall nitudes ≤23.0 in the F613W filter, and with magnitude errors
fraction of catastrophic outliers no higher than 3percent. In this <0.5. Above this magnitude limit, the reliability of signal-to-
work,weusedtheBPZmeasuredphoto-zs,andthesamephotome- noise (S/N) measurements, photo-z estimations, and geometrical
trythatwasusedinphoto-zestimations.Allinformationaboutthe extended/point-sourceclassificationsdecreasesignificantly.Selec-
photo-zmeasurementsandcataloguescanbefoundinMolinoetal. tionoftheF613WfilterisexplainedinSection3.2.2.Incomparison
(2013). with previous criteria, magnitude selection is the most restrictive
one.
(iv) Flag tests. In the sample selected through previous con-
2.1 Sampleselection
ditions, 57percent of sources are ‘good detections’ (SEXTRACTOR
Wecarriedoutthemorphologicalclassificationonawell-defined FLAG parameter = 0), 27percent are possibly blended sources
sample,freeofstars,havingreliablephoto-zandphotometricmea- (FLAG=2),15percent,plusblending,havecloseneighboursor
surements.Inthissection,wedescribethedifferentselectioncriteria badpixels(FLAG=3),while<1percenthaveotherFLAGvalues.
appliedtoobtainthefinalworkingsample. We classified morphologically all galaxies independently of their
FLAG parameter, including close/interacting systems. However,
(i) Extended–point-like source separation. To discriminate be-
thefinalstatistics(Sections5and6),andthepublishedcatalogue
tween the extended and point-like sources, we used the classifi-
(AppendixA)includeonlysourceswithFLAGvalues0and2(see
cation provided by Husillos et al. (in preparation). In all obser-
Section4).
vations,asampleofmorethan2000real,bright,unsaturatedand
geometricallycircularpoint-likeALHsourceswasused,scaledto The final selected sample to be morphologicaly classified has
lowerrandomfluxes,andinjectedintotheemptyregionsofopti- 43665galaxies.
calimage.Afterthis,thesourceswererecovered,usingthesame
parametrization as applied in source detection. Point-like sources
3 MORPHOLOGICAL CLASSIFICATION
areconsideredassuccessfullyrecoveredwhendetectedatdistances
OF GALAXIES
lowerthan1arcsecfrompreviousinjectioncoordinates.Thesame
procedurewasimplementedforextendedsources.Finally,thelo-
3.1 Generalmethodology
cus of point-like and extended sources was defined in the appar-
ent magnitude and surface brightness space (MAG_AUTO8 and The main tool used in this work to estimate morphologies is
MU_MAX9parameters,respectively,obtainedbySEXTRACTOR).By GALSVM, a non-parametric support vector machine (SVM) based
plottingeachlocus,itispossibletoestimatethepoint-likesource code(Huertas-Companyetal.2008,2009,2011).Basically,GALSVM
contamination in a quantitative way. GEOM_CLASS_STAR pa- usesatrainingsetoflocalgalaxieswithknownvisualmorpholo-
rameterwasdefinedinthisway,havingvalueequalto1forpoint gies to train the SVM that is then applied to the data set to be
classified (see Section 3.2.1). Galaxies from the training sample
are redshifted and scaled in luminosity to match the magnitude
7http://alhambrasurvey.com/ countsandredshiftdistributionoftheALHsample,resampledwith
8Kron-like elliptical aperture magnitude [see Bertin (2009) and Hol- theALHpixelscale(∼0.222arcsecpixel−1),andfinallydropped
werda (2009) SEXTRACTOR manuals at https://www.astromatic.net and
http://mensa.ast.uct.ac.za/∼holwerda/Site/Source_Extractor.html, respec-
tively]. 10Definestheredshiftconfidencelimits,wheregalaxieswithhigherODDS
9Peaksurfacebrightnessabovebackground(seethemanualsgiveninfoot- haveamoresecureredshiftestimations(seeBen´ıtez2000,formoreinfor-
note8). mation).
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3448 M.Povic´ etal.
Figure2. MagnitudeandredshiftnormalizeddistributionsoflocalsampleusedbyGALSVM(greensolidlines).Thissamplewasscaledinluminosityand
redshifted(blacksolidlines)tomatchthedistributionsoftheALHsample(reddashedlines).ShownexamplecorrespondstodistributionsintheALH-7field.
in a real ALH background. Fig. 2 shows an example of redshift 3.2 GALSVMCONFIGURATIONANDCLASSIFICATION
andmagnitudedistributionsoflocalsamplebeforeandafterbeing
3.2.1 Trainingsampleoflocalgalaxies
redshifted and scaled in luminosity (green and black solid lines),
andcorrespondingdistributionsoftheALH-7sample(reddashed We use a local sample of 3000 visually classified galaxies
lines). (0.01≤z≤0.1)takenfromtheNair&Abraham(2010,hereafter
Wemeasuredsevenmorphologicalparametersonthissimulated N&A)cataloguewhichcontains∼14000galaxiesfromtheSDSS.
dataset(andontheALHsamplelateron),andusethemsimulta- Thenumberofgalaxiesusedfortrainingisselectedasatrade-off
neouslytotrainthevectormachine: betweenclassificationaccuracyandcomputingtime.Thecomput-
ingtimetotraintheSVMwiththecurrentalgorithmisindeedvery
(i) Ellipticity sensitive to the size of the training data set. We therefore chose
(ii) Abraham concentration index (CABR) – ratio between the objectsrandomlyandselectthesamenumberofET(ellipticaland
fluxesat30and90percentoftheradius(Abrahametal.1996) lenticular)andLT(spiralandirregular)galaxies,toavoidproblems
(iii) Conselice–Bershady concentration index (CCON) – ratio related to unbalanced data sets, which is required for GALSVM to
betweencircularradiicontaining20and80percentofthetotalflux workproperly.Wecheckedthattherandomlyselectedsubsampleis
(Bershady,Jangren&Conselice2000) representativeintermsofgeneralproperties(colours,magnitudes,
(iv) Gini (GINI)– cumulative distributionfunction of galaxy’s etc.)ofthecompletedataset.Fig.3showsthesecomparisonsfor
pixelvalues(Abrahametal.2003) the g-band magnitude, redshift, g − r colour, N&A morphologi-
(v) Asymmetry(ASYM)–measuresthedegreeofasymmetryin calclassificationandinclination(forLTgalaxiesonly).Moreover,
thelightdistribution(Conseliceetal.2000) wealsocomparedmagnitudes,redshifts,andcoloursofETandLT
(vi) Smoothness(SMOOTH)–measurestherelevanceofsmall- galaxiesinboth,selectedandfullsamples.Ascanbeseenfromnor-
scalestructures(Conseliceetal.2000),and malizeddistributions,thepropertiesofrandomlyselectedandfull
(vii) M20momentoflight(M20)–fluxineachpixelmultiplied N&Asamplesareconsistentinallplots,asstatedbyKolmogorov–
bythesquareddistancetothecentreofthegalaxy,summedover Smirnov(hereafterKS)testswhichprovesthatnoselectionbiases
the20percentbrightestpixelsofthegalaxy(Lotzetal.2004). areintroduced.
N&A classification was obtained in the SDSS g-band images,
Even though several of these parameters appear to be redun- while our classification was carried out in the F613W band (cor-
dant, SVM were specially designed to be robust to redundancies respondstoSDSSrband;seeSection3.2.2).Takingintoaccount
in the feature space (see e.g. Huertas-Company et al. 2008). The thatweareperformingbroadclassification,separatingallsources
set of parameters used to obtain the morphological classification intoETsandLTgalaxies,thedifferencebetweenthegandrbands
wastestedinPovic´etal.(2012).Mentionedparametersweremea- inspectedvisuallyisinsignificantforourwork.Foreachmorpho-
suredforboth:localsample(redshiftedandscaledinluminosity) logicaltypefromN&Acatalogue,weselectedrandomly50sources
and the ALH sample that we want to classify. Using vector ma- andwechecked(between4people)theirimagesinallfiveSDSS
chine, and comparison between the GALSVM classification of lo- bands.Inallcases,wedonotseeanydifferencesingalaxystruc-
cal galaxies and their original, visual one, we can then classify turesbetweentheg,randibands.Ontheotherhand,wedosee
ALH galaxies. The output of the classification step is a proba- significant changes between any of these three bands and u or z
bility value to be in a given class for each object. The probabil- filters.
ity takes values from 0 to 1 (99.9 for unclassified objects). The Theselectedlocaldatasetisthenredshifted,scaledinluminosity
robustness of the classification and the sensitivity to the train- and dropped in the ALH fields. To that purpose, GALSVMrequires
ing set is estimated by repeating the classification several times the apparent magnitude and photo-z distributions of all sources.
through Monte Carlo runs (hereafter MC) with slightly different Toimprovestatistics,weusedforeachfieldthemagandphoto-z
trainingsets(seeHuertas-Companyetal.2011,formoredetails). distributionsofthefourCCDsaftercheckingthatthesedistributions
Wedescribeinthefollowingthespecificconfigurationusedinthis arecompletelyconsistentwiththoseofindividualfields(individual
work. CCDs). Before dropping the mock galaxies they are re-sampled
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ALHAMBRA:morphologicalclassification 3449
Figure3. ComparisonbetweenthepropertiesofthefullN&Asample(blacksolidlines)and3000galaxies(reddashedlines)selectedrandomlyfromthefull
onetobeusedinourmorphologicalclassification.g-bandmagnitude(topleft),redshift(topright)andg−rcolour(middle)distributionsarerepresentedfor
allgalaxies(leftsubdiagram),ETs(middlesubdiagram)andLTs(rightsubdiagram).Wealsocomparedmorphologicaltypes(forallgalaxies;bottomleft)and
inclinations(forLTgalaxiesonly;bottomright)distributions.Maximumdeviation(D)andprobabilitythattwodistributionsarethesame(prob;takesvalues
between0and1,wheresmallvaluesshowthatthecumulativedistributionfunctionofselectedsampleissignificantlydifferentfromthefullsample)areresults
oftheKSstatistics,showinginallplotsthatpropertiesofourselectedN&Asamplearecompletelyconsistentwiththefullone.
with the same pixel scale than the ALH galaxies and convolved galaxiesathigherredshift(sinceweareusingverybrightgalaxies).
withaPSFtomatchthesamespatialresolution.Inordertocope Bydroppingthegalaxyinarealbackground,weexpecttoreproduce
with the k-correction, we select the SDSS filter which is closer atbestallthedifferentnoisesfromtherealimages.
to the wavelength the ALH filter is probing given the redshift of Weassumedthatthereisnochangeingalaxypropertiesbetween
thegalaxy.Surfacebrightnessdimmingistakenintoaccountwhen the local and high-redshift samples (e.g. luminosity–morphology
scalingthegalaxyinfluxsinceweempiricallymatchthemagnitude dependence).Thisassumptionmightbestrongforourredshiftrange
countsoftheALHsurvey(wedonotintroduceanysizeevolution (∼0–1), but can be justified since we are classifying all galaxies
though). Concerning the noise, we make the hypothesis that the intotwobroadmorphologicaltypes.Moreover,weonlyusedmor-
noise from SDSS galaxies is negligible compared to the noise of phological parameters in our classification, excluding luminosity.
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3450 M.Povic´ etal.
This minimizes significantly the luminosity–morphology depen-
dence;however,itdoesnoteliminateitcompletely(e.g.stillhigher
redshiftsamplewillcontainmoreluminoussources,besides,forthe
redshiftedtrainingsamplewearetryingtoreproducethemagnitude
distributionofALHsourcesassumingthatluminosity/morphology
relationsaresimilar).Tominimizeevenmoretheluminosityeffects
onourmorphologicalclassification,weforcedtheGALSVMtoselect
∼50percentofETandLTlocalgalaxiesineachtrainingMCrun.
Although, we used 3000 local galaxies in this work, a larger
samplewasconstructedfromtheN&AandEFIGI11(Baillardetal.
2011)samples,withthetotalof15036sourcesdowntoredshifts
z ≤ 0.1. The EFIGI visual classification was used at redshifts
z < 0.01, including all galaxies with good morphological classi-
fication, and excluding dwarf objects, while N&A galaxies range
between0.01≤z≤0.1.Atthemoment,thisisthemostcomplete
localsamplethatcanbeusedtotestthemorphologyofhigh-redshift
galaxieswiththeGALSVMcode,includingforeachsourcethelistof
astrometric,photometric,redshift,morphologicaltype,sizeparam-
eters,poststamps,masksandPSFimagesinallfiveSDSSbands.
3.2.2 GALSVMclassification
ThefinalclassificationisperformedintheALHF613Wband,since
theS/Nratioishigherinthisfilter,asshowninAparicio-Villegas
etal.(2010).ForeachGALSVMrun,wesetthequalityparametersthat
correspondtousedALHimage:filtername,centralwavelength,full
widthhalf-maximum,pixelscale,sigma,zero-pointandsaturation
level.GiventhetypicalresolutionoftheALHsurvey(mainlyaround Figure4. Fromtoptobottom:distributionsofF613Wmagnitudes(left)
1.0arcsec,seeTable1)andthedepth,wehaverestrictedinthiswork andcorrespondingredshifts(right)intheALH-4fieldforsixmagnitude
cutsusedinourmorphologicalclassification:≤20.0,≤21.0,≤21.5,≤22.0,
to two broad morphological classes: ET and LT. For each source
≤22.5and≤23.0.
in the final selected sample (see Section 2.1), we compute seven
morphologicalparametersdescribedinSection3.1.
4 CALIBRATION OF THE MORPHOLOGICAL
Since the sample is fully dominated by faint objects given the
CLASSIFICATION USING COSMOS/HST DATA
shape of the magnitude counts (Fig. 4), when fainter objects are
included in the classification, the algorithm will be optimized to TheclassificationiscalibratedintheALH-4fieldusingtheclassi-
classifythesegalaxiesandthefractionofmisclassifiedbrightob- ficationofthesameobjectsobservedwithHST/AdvancedCamera
jectsmightsignificantlyincrease.Toavoidthiseffect,weperformed forSurveys(ACS)intheCOSMOSsurvey.Themorphologicalclas-
themorphologicalclassificationwithsixincreasingmagnitudecuts: sificationinCOSMOSusedherewascarriedoutwithGALSVMby
≤20.0,≤21.0,≤21.5,≤22.0,≤22.5and≤23.0.Thatway,bright Huertas-Companyetal.(2009),andispubliclyavailablethroughthe
galaxies are classified using a training set made only of bright Tascaetal.(2009)catalogue.12 Theclassificationseparatesgalax-
galaxies.Thesecutsalsocorrespondroughlytoincreasingredsh- iesintothreeclasses(early,lateandirregulars)butdoesnothave
fits:≤0.7,≤0.9,≤1.0,≤1.2,≤1.2and≤1.3. probabilistic information though since it was done with an older
Thefinalprobabilityforeachgalaxyiscomputedastheaverage versionofGALSVM.
oftheoutputprobabilityof15MCindependentruns.Again,this ByusingCOSMOSasthereferencesample,weexplicitlyneglect
numberisanempiricaltrade-offbetweencomputingtimeandac- the classification errors of the galaxies in COSMOS. This choice
curacy,andisaresultofprevioustestscarriedoutwith10,15and is justified since we focus here on bright galaxies with only two
20runs.IneachMCrun,GALSVMselectsrandomlyabalancedset morphologicalclasses.Wealsoneglecttheeventualmorphological
of2000outof3000inputlocalgalaxies.Theresultofeachrunisa drift between the morphologies in COSMOS (F814W) and ALH
probabilityforaALHgalaxytobeET,whereforsmallprobability (F613W).Again,sincewearesplittingourgalaxiesintwoclasses
values increases the possibility to be an LT. At the end, for each wedonotexpectasignificanteffect.Thisassumptionisindeedcon-
ALHsourcewecomputethefinalprobabilitytobeET(hereafter firmedthroughextensivetestsontheSDSSdataset.Wecompared
pE)asaveragevalueof15probabilities,andtheprobabilityerroras differentmorphologicalparametersoflocalN&Agalaxiesbetween
thescatterofthedistribution.Obviously,sincewedealwithatwo- therandibands,withoutfindinganysignificantdifferencesintwo
classproblemonly,theprobabilityforthesourcetobeLT(hereafter bands,independentlyoftheredshiftandtheHubbletype.
pL)issimplypL=1−pE. Fig.5showsthedistributionsofpEderivedintheALH-4fieldfor
threemorphologicaltypesclassifiedintheCOSMOS/HSTsurvey
12http://irsa.ipac.caltech.edu/data/COSMOS/tables/morphology/cosmos_
11http://www.astromatic.net/projects/efigis morph_tasca_1.1.tbl
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ALHAMBRA:morphologicalclassification 3451
Figure5. ComparisonbetweentheGALSVMclassificationintheALH-4andCOSMOSsurveysfordifferentF613Wmagnitudecuts:≤20.0(topleft),≤21.0
(topright),≤21.5(middleleft),≤22.0(middleright),≤22.5(bottomleft)and≤23.0(bottomright).Ineachplot,wecomparethedistributionsofaveraged
probabilities(PROB_AVG)measuredintheALH-4fieldforthreemorphologicaltypesclassifiedintheCOSMOSfieldusingtheHST/ACSimagingdata
(Huertas-Companyetal.2009;Tascaetal.2009):elliptical/S0(redsolidlines),spiral(bluedashedlines)andirregular(greendottedlines)galaxies.
withGALSVMcode:E/S0(redsolidlines),spirals(bluedashedlines) Table2. p10thresholdforselectingETandLTgalaxies(p10sample)
and irregulars (green dotted lines). Following these distributions, indifferentmagnitudebins.
welookfortheprobabilitythreshold(pE,pL)toapplytotheALH
th th
classificationsothattheresultingclassificationcontainslessthan Mag.limit 20.0 21.0 21.5 22.0 22.5 23.0
10percent contaminations from the neighbouring morphological
pE >0.6 >0.7 >0.75 >0.8 – –
type.Tothatpurpose,foragivenprobabilitythresholdpE,pL we 10
definethefollowingparameters: th th p1L0 >0.7 >0.7 >0.6 >0.5 >0.5 >0.5
(i) Truepositives(tp):galaxieswithp >pE(p >pL)inALH
E th L th Hereafter,asampleclassifiedwithp thresholdswewillcallp10
whichareclassifiedET(LT)inCOSMOS; 10
(ii) True negatives (tn): galaxies with p <pE (p <pL) in sample.Thisisasamplethatwereleaseinpaperandwhichweanal-
E th L th yseinthefollowingsections,asweexplaininSection5.However,
ALHwhichareclassifiedLT(ET)inCOSMOS;
(iii) False positives (fp): galaxies with p >pE (p >pL) in inthesamewayweobtainedthep10thresholds,wemeasuredalso
E th L th thethresholdsathighercontaminationlevels.Wecomparestatistics
ALHwhichareclassifiedLT(ET)inCOSMOS;
(iv) False negatives (fn): galaxies with p <pE (p <pL) in in Section 5, while the full catalogue of whole >44 000 selected
E th L th sourcescanbeobtainedthroughdirectcontact.
ALHwhichareclassifiedET(LT)inCOSMOS.
Each probability threshold, has a corresponding completeness,
Thepurity(P,fractionofwell-classifiedobjectsamongallobjects which is reported later in Table 4. For the p10 sample, the com-
classifiedinagivenclass)andthecompleteness(C,fractionofwell- pleteness varies from ∼70percent for the brightest objects to
classifiedobjectsamongallobjectsreallybelongingtoagivenclass) ∼30percentatthefaintend.Thesevaluesbecomeslightlyworse
arethereforedefinedasfollows: if galaxies from CCD3 are included, since it is degraded com-
fp paredtotheothers.13Allstatisticsarecarriedoutusingonly‘good
P =1− fp+tp (1) detections’, i.e. FLAG = 0 (see Section 2.1). However, a similar
accuracyisobtainedwhen‘blended’(FLAG=2)sourcesarein-
tp cluded. Therefore, in the following sections and in the published
C = fn+tp, (2) catalogue (Appendix A) we use both FLAG = 0 and FLAG = 2
sources.
pE andpL arethereforethethresholdstoapplysothatthecon- Asasanitycheck,weperformedavisualinspectionofp10classi-
10 10
taminationislowerthan10percent(P>0.9).Table2showsthe fiedsampleintheALH-4field(overlapswithCOSMOS).Weused
values obtained for the different magnitude cuts and for the two
morphological classes. Note that for ET galaxies fainter than 22,
thecontaminationofLTgalaxiesisalwaysabove10percent. 13http://www.caha.es/CAHA/Instruments/LAICA
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3452 M.Povic´ etal.
theHST/ACSimagesofallETs(86intotal)andof300randomly
selectedLTs(20percentofthetotalp10LTsampleclassifiedinthe
ALH-4field).Visualclassificationwasperformedbyfivepersons
lookingeachobjectindividually.Byeachclassifierwemeasuredthe
populationofpossiblemisclassifiedsources(withineachtype),and
thenweobtainedtheaveragedone:14.0±6.0and7.8±4.8percent
forETandLTclassifiedsources,respectively,whichisinagreement
withthestatisticspresentedabove.Figs6and7showasampleof
selectedETandLTgalaxyimages,respectively,indifferentmag-
nitudebins.ForeachALHsource(topimages),weshowalsothe
correspondingHST/ACSimage.
5 MORPHOLOGICAL CLASSIFICATION
IN ALH
Aftercalibratingourmorphology,wethenrunintheconsistentway
the GALSVM code in whole ALH survey. In seven ALH fields, we
settheGALSVMconfigurationfiles,andforalldetectionswemea-
suredmorphologicalparametersandaveragedprobabilities.Given
the number of fields/observations and magnitude cuts (see Sec-
tion3.2.2),weobtained288catalogues.
Intotal,withGALSVMweobtainedtheclassificationfor85percent
ofselectedsources(withdifferentlevelsofcontamination),while
15percentstayedunclassified.Sourcesforwhichwewereableto
measuremorphologicalparametershavedifferentlevelsofcontam-
inationbyothermorphologicaltype.Weanalysedtheunclassified
sourcesintheALH-4field(overlapswithCOSMOS)byusingthe
HST/ACSimages,detectingthat∼80percentofsampleareinter-
action/mergercandidates.Inaddition,∼40percentofunclassified
sourcesweredetectedwiththeCCD3,whichingeneralhaslower
Figure6. SampleofETgalaxiesindifferentmagnitudebins,observedin S/NincomparisonwiththeotherthreeCCDs(asalreadymentioned
theALH-4(topimages,F613Wband)andCOSMOS/HST(bottomimages, above).ThepopulationofET/LTsourceswemightbemissingfor
F814Wband)surveys. beingunclassifiedwithGALSVMisabout3percent.
Figure7. SameasinFig.6,butforgalaxiesclassifiedasLT.
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ALHAMBRA:morphologicalclassification 3453
Table3. Populationofsourcesclas- taminationdoesnotgoabove20percent;soingeneral,ifweare
sifiedasETorLTwithdifferentlev- able to detect LT galaxy structures and classify the galaxies, the
elsofcontaminationbyothertype. contamination will always be low. When this is not the case, we
have a contamination of ET sample with LT sources. Finally, for
Contaminationlevel Population thep10galaxiesweexpecttohaveacontaminationbyothertype
<10percent 61percent lowerthan10percent,andcontaminationofstarsbelow1percent
<20percent 73percent downtomagnitudes22.0and5–7percentbetweenmagnitudes22.0
<30percent 74percent and23.0.(seeSection2.1).Thislow-contaminationcatalogueisthe
<40percent 77percent onewearereleasingwiththispaper.Descriptionofallcolumnsand
<50percent 82percent asampleofcatalogueforthefirstfiveobjectsareavailableinAp-
>50percent 18percent pendixA.Thefullp10catalogueof22051galaxiesisavailablein
theelectronicversionofthispaperandthroughtheALHwebsite
http://alhambrasurvey.com/.
Foreachmagnitudebinwethenappliedthep10 thresholds,ob- Wedonottreatmergersspecificallyinourclassification.How-
tained in the HST/ACS calibration phase to ensure a 10percent ever,wedominimizetheirpopulationbyexcludingfromourp10
contaminationlevel(p10sample;seeTable2).Withthiscontami- classifiedsampleallsourceswithSEXTRACTORFLAGparameter>2
nation,inallfieldswekeepclassified22051galaxies,61percent (includeobjectswithcloseneighbours).Theydoenterinthetotal
ofallclassifiedsources,asshowninTable3(tablealsoprovidesthe selectedsample,butweexcludedthemfromallanalysispresented
summaryonothersourcesclassifiedasET/LTwithhighercontam- inthispaper(seeSection4).Moreover,wesawthatmanyinteract-
inationlevels).Ofthose,1640and10322sourcesareclassifiedas ingsystemsstayedunclassifiedwithGALSVM.Asmentionedabove,
ET(photo-z<0.5)andLT(photo-z<1.0),respectively,downto ∼80percentofallunclassifiedsourcesshowclearsignsofdistor-
magnitudes22.0intheF613Wband.Forthismagnitudelimit,the tions,closecompanions,areedge-onsystems,oramixture.Finally,
fractionofourETgalaxiesrespecttoLTsis∼16percent[consistent wecheckedtheHST/ACSimagesofallp10galaxiesthatoverlap
withtheresultsobtainedbyHuertas-Companyetal.(2009)inthe withtheCOSMOSfield,inordertoquantifythepopulationofpos-
COSMOSfieldusingthesamemethodofgalaxyclassification].In sibleinteractions.Fromindependentinspectionscarriedoutbyfive
addition,formagnituderange22.0<F613W≤23.0weclassified persons,weobtainedtheaveragedpopulationof15.0±2.5percent
other10089LTgalaxieswiththesamelevelofcontaminationof merger/interactioncandidatesbetweenthegalaxiesclassifiedasLT.
10percent and down to photo-zs of 1.3. For the rest of the sam-
ple,thecontaminationishigher(includingalsoclassifiedsources
withveryclosecompanions(FLAG≥3)whichweexcludedfrom 5.1 Selectioneffects
thep10sample),asshowninTable3,beingingeneralaffectedby
Sincethefinalcatalogueisobtainedafterapplyingarbitraryproba-
theresolutionofourdataandbytheworstqualityofCCD3data.
bilitycuts,itmightbeaffectedbynon-trivialselectioneffectswhich
Table 4 shows the number of objects and completeness of each
weinvestigateinthefollowingfromthepointofviewofredshift
morphologicaltypeineachmagnitudebin.Besidestheinformation
andsizedistributions.
aboutp10sample,wealsoprovidethestatisticsathighercontam-
ination levels. As expected, the level of contamination is directly (i) Redshift.Figs8and9showforeachanalysedmagnitudebin,
related with the magnitude, where fainter objects were classified the redshift distributions of ET and LT galaxies, respectively, of
withpoorerprobabilities.Athighercontaminationlevelswehave thefullsampleascomparedtothefinalp sampledefinedinthe
10
problemsclassifyingETsources,sincemanyLTsstarttomixwith previoussection.Foratotalsample(blacksolidlines),theclassi-
ETsgivingworstprobabilitydistributions.ForLTgalaxies,thecon- ficationisdirectlyobtainedfromtheTascaetal.(2009)catalogue
Table4. NumberandcompletenessofETandLTgalaxies,classifiedineachmagnitudebininrelationwiththecontaminationlevel.Besideeachnumberwe
alsorepresentthepercentageofgalaxiesrespecttothetotalnumberofsourcesintheobservedcontaminationcategory(lastcolumn).
F613Wmagnitude ≤20.0 20.0–21.0 21.0–21.5 21.5–22.0 22.0–22.5 22.5–23.0
Contamination≤10percent 715(44percent) 549(33percent) 219(13percent) 157(9percent) – – 1640
C<10 0.7 0.4 0.4 0.3 – –
Contamination≤20percent 1103(28percent) 1841(47percent) 622(16percent) 252(6percent) 112(3percent) – 3930
ET C<20 0.7 0.4 0.4 0.3 0.1 –
20<Contamination≤50percent – – 706(21percent) 1609(47percent) 1081(32percent) – 3396
C20−50 – – 0.6 0.5 0.4–0.5 –
Contamination>50percent – – – 492(7percent) 1877(28percent) 4297(65percent) 6666
C>50 – – – 0.7 0.6–0.7 0.8
Contamination≤10percent 1082(5percent) 2818(14percent) 2689(13percent) 3733(18percent) 4828(24percent) 5261(26percent) 20411
C<10 0.7 0.7 0.7 0.7 0.6 0.3
Contamination≤20percent 1720(7percent) 3432(15percent) 3032(13percent) 3659(16percent) 4966(22percent) 6177(27percent) 22986
LT C<20 0.7 0.7 0.7 0.7 0.6 0.3
20<Contamination≤50percent – – – – – –
C20−50 – – – – – –
Contamination>50percent – – – – – –
C>50 – – – – – –
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