The ALHAMBRA survey: reliable morphological catalogue of 22 051 early

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 D
o
w
R. M. Gonza´lez Delgado,1 L. Infante,14 C. Lo´pez-Sanjuan,5 V. J. Mart´ınez,7,15 nlo
a
I. Matute,1 I. Oteo,4,11 A. M. Pe´rez-Garc´ıa,4,13,16 F. Prada1 and J. M. Quintana1 de
d
1InstitutodeAstrof´ısicadeAndaluc´ıa(IAA-CSIC),E-18008,Granada,Spain from
2GEPI,Paris-MeudonObservatory,F-75014Meudon,France h
3UniversityofParis,F-75205Paris,France ttp
s
45ICnesntittruotodedeEAstsutdroiof´ıssidceaFd´ıesiCcaandaerliCasos(ImAoCs)d,eE-A3r8a2g0o´0nL(CaELFagCuAn)a,,ET-e4n4e0r0if1e,TSepruaeinl,Spain ://ac
a
6InstitutodeF´ısicadeCantabria(CSIC-UC),E-39005Santander,Spain d
e
7UnidadAsociadaObservatoriAstrono`mic(IFCA–UV),Valencia,Spain m
8Observato´rioNacional-MCT,RuaJosCristino,77.CEP20921-400RiodeJaneiro-RJ,Brazil ic.o
9DepartmentofTheoreticalPhysics,UniversityofBasqueCountry,UPV/EHU,POBox644,E-48080Bilbao,Spain up
10IKERBASQUE,BasqueFoundationforScience,AlamedaUrquijo36-5,E-48008Bilbao,Spain .co
11FacultaddeF´ısica,DepartamentodeF´ısicaAto´mica,MolecularyNuclear,UniversidaddeSevilla,E-41012Sevilla,Spain m
/m
12InstitutdeCie`nciesdel’Espai,IEEC/CSIC,E-08193Barcelona,Spain n
13DepartamentodeAstrof´ısica,FacultaddeF´ısica,UniversidaddelaLaguna,E-38200LaLaguna,Spain ra
s
14DepartamentodeAstronom´ıa,PontificiaUniversidadCato´lica,SantiagodeChile,Chile /a
15ObservatoriAstrono`micdelaUniversitatdeVale`ncia,Valencia,Spain rtic
16Asociac´ıonASPID,ApartadodeCorreos412,E-38200LaLaguna,Tenerife,Spain le-a
b
s
Accepted2013August13.Received2013August5;inoriginalform2013April9 tra
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ABSTRACT /3
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Advanced Large Homogeneous Area Medium Band Redshift Astronomical (ALHAMBRA) 4
/1
isphotometricsurveydesignedtotracethecosmicevolutionandcosmicvariance.Itcoversa 0
3
largeareaof∼4deg2 ineightfields,wheresevenfieldsoverlapwithothersurveys,allowing 50
5
7
ustohavecomplementarydatainotherwavelengths.Allobservationswerecarriedoutin20 b
y
continuous,mediumband(30nmwidth)opticaland3near-infrared(JHK)bands,providing g
u
theprecisemeasurementsofphotometricredshifts.Inaddition,morphologicalclassification es
ofgalaxiesiscrucialforanykindofgalaxyformationandcosmicevolutionstudies,providing t on
1
the information about star formation histories, their environment and interactions, internal 1
perturbations, etc. We present a morphological classification of >40 000 galaxies in the Ap
ALHAMBRA survey. We associate to every galaxy a probability to be early type using the ril 2
0
automatedBayesiancodeGALSVM.DespiteofthespatialresolutionoftheALHAMBRAimages 19
(∼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
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,
D
omyisapowerfultoolinordertounderstandthephysicsbehindthe Primack&Madau2004;Scarlataetal.2007).Currentworksareled o
w
formationandevolutionofthestudiedobjects.Probablythemost to more complex fitting models, using orthonormal mathematical n
lo
popular classification of galaxies is based on the shapes or mor- basestodecomposethegalaxies,andthencorrelatingthephysical a
d
phologies(startedwithReynolds1920;Hubble1926,1936).This properties of the objects with the coefficients of the decomposi- ed
first order classification has survived over time since the differ- tionbymeansofaprincipalcomponentanalysis.Thisisthecase fro
m
entmorphologicalclassesofgalaxieshavealsodifferentphysical of the shapelets (Kelly & McKay 2004, 2005; Andrae, Jahnke &
h
properties and probably different evolutionary tracks. In general, Melchior 2011a), or future works with the sersiclets (Ngan ttp
s
galaxies can be divided into two main classes: early-types (here- et al. 2009; Andrae, Melchior & Jahnke 2011b), and the CHEFs ://a
afterETs)andlate-types(hereafterLTs).ETsincludeellipticaland (Jime´nez-Teja&Ben´ıtez2012).Themainadvantageoftheselatter c
a
lenticulargalaxies,whileLTsincludespiralsandirregulargalaxies. methodswithregardtotheformeristheirflexibilityandreliabilityto de
m
ETsappeartobeafamilyofobjectsshowingoldstellarpopulations, reproduceeveryfeatureinthegalaxies,andthentoefficientlymodel ic
spheroidal-like dynamical properties and a small fraction of cold every kind of morphology, including irregular objects. Moreover, .o
u
gaswhereasLTsaremoregas-richobjects,presentyoungerstellar they do not need any a prioriknowledge and they donot impose p.c
populations,andaremainlyrotationsupported. anyprofiletofitthegalaxies.However,thesemathematicalmodels om
Visual inspection is the traditional way to classify galaxies. arecomputationallymoreexpensiveandtheparametersofthefinal /m
n
Bydefinition,itissubjectiveand notreproducible,butforbright decompositiondonotprovideanyphysicalinformation.ALHdata ra
s
and extended objects there is a general good agreement between willbeusedforfurtherdevelopmentoftheCHEFsmethod(Jime´nez /a
differentobservers.Theyarehowevertimeconsuming.Inthepast, etal.,inpreparation). rtic
galaxysamplescontainedfromdozensuptohundredsofgalaxies GALSVM(Huertas-Companyetal.2008)providesageneralization le-a
whilepresentgalaxysurveyshaveuptomillionsofgalaxies.This of the non-parametric classifications by using an unlimited num- bs
makesimpossibletogivedetailedmorphologicalclassificationsun- ber of dimensions and providing a probabilistic output following tra
c
lessalargeamountofclassifiersareinvolved(seetheGalaxyZoo a Bayesian approach (see also Fasano et al. 2012 for a similar t/4
3
project;Lintottetal.2008). approach). Thealgorithmhasbeenshowntobeespeciallyuseful 5
/4
Overthepastfewyears,differentautomatedmethodsofmorpho- whendealingwithlow-resolutionandhigh-redshiftdata(Huertas- /3
4
logicalclassificationofgalaxieshavebeendeveloped.Automated Company et al. 2009) and has been successfully applied to sev- 4
4
classifications resolve the two main problems raised above. They eral large samples at low and high redshift (Huertas-Company /1
0
provideindeedreproducibleinformation,andtheerrorscanbefully et al. 2009, 2011), including the Cosmological Evolution Survey 3
5
understoodbyusingextensivesimulations(e.g.Trujilloetal.2001; (COSMOS;1 Scoville et al. 2007) and Sloan Digital Sky Survey 05
7
Simard et al. 2002, 2011). In addition, modern galaxy classifica- (SDSS;2Castander1998)samples. b
y
tionalgorithmsareabletoclassifylargesamplesofgalaxiesina In this paper, we present the morphological classification of a g
u
reasonableamounttime. largesampleofgalaxiesfromtheAdvancedLargeHomogeneous e
s
Weusuallydistinguishbetweenthreebroadgroupsofautomated AreaMediumBandRedshiftAstronomical,ALHAMBRAsurvey t o
n
galaxyclassifications:parametricbasedongalaxyphysicalparam- (hereafter ALH; see Moles et al. 2008), located at different red- 1
1
eters, non-parametric and parametric based on mathematical pa- shifts.TakingintoaccounttheresolutionofALHdata(∼1arcsec) A
p
rtoamreepterorsd.uPcaerasmometerigcaclalaxsysimficeaatsiuornesmuesnetps.arOanmeeotrficthmeomdoelsstipnooprudlaerr tahnidsmalletthhoedafdovraonutargmesoropfhGoAloLSgVicMalccoldaessmifiecnattiioonne.dTahbeoAveL,Hwseuurvseedy ril 20
parametricmethodsbasedongalaxyphysicalparametersclassifies imaged ∼4 deg2 of the sky through 23 optical and near-infrared 19
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/
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
D
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Figure1. Exampleoftwogalaxiesobservedwith20opticaland3NIRALHfilters. 4
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/1
0
3
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is described in Section 3, and the main results are discussed in from340to970nm,withnon-overlappingandequal30nmwidth 05
7
Sections4–6.Finally,AppendixAdescribesthecontentofthefirst medium bands (Aparicio-Villegas et al. 2010). This set of opti- b
y
publishedmorphologicalcatalogueofgalaxiesintheALHsurvey. calfilterswasspeciallydesignedfortheALHsurveytoachievea g
Weassumedthefollowingcosmologicalparametersthroughout goodaccuracyofphotometricredshiftsof∼δz/(1+z)=0.015for ue
s
thepaper:(cid:3)(cid:4)=0.7,(cid:3)M=0.3andH0=70kms−1Mpc−1.Unless galaxiesbrighterthanF814W≤24.5,threetimesbetterthantheone t on
otherwisespecified,allmagnitudesaregivenintheABsystem(Oke achievedwhenusingthe4–5filtersystems(formoreinformation, 1
1
&Gunn1983). seeBen´ıtezetal.2009).Fig.1showstheexampleoftwogalaxies A
p
obsOeprvtiecdalidnaatlalwAeLreHobbasnerdvse.dwithLargeAreaImagerforCalarAlto ril 20
2 DATA (LAICA4),withthetotalexposuretimeof100000sperpointing. 19
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).
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 D
Ben´ıtezetal.2004).BPZwasrunonaseparatedpointspreadfunction with good photo-z measurements. First, we only selected those ow
n
(PSF)correctedphotometryandanewlibraryoftemplates(Ben´ıtez sources detected in all ALH filters. And secondly, we selected lo
a
etal.,inpreparation)wasimplemented,composedby11SEDs(4el- sources with BPZ_ODDS10 parameter above 0.2. With these two d
e
ldirpatwicnalf,r1olmenPtEicGuAlSaEr,(1FSiobcc&,1RScodccaan-dV4olsmtaerrbaunrgsteg1a9la9x7i)e,sb)u,otrtihgeinnaolply- certiatel.ri2a,01w3e).eWxpeeccthteockheadvethlaetssretqhuaniri3ngpetrhceeonbtjoefctosuttoliebresd(Meteoclitneod d from
timizedusingtheFIREWORKSphotometryandspectroscopicredshifts inallALHbandsintroducesonlyasmallselectionbiasaffecting h
fromWuytsetal.(2008).Basedonasampleof∼7000galaxieswith thesampleofredgalaxiesatz>0.4andmagnitudes<22inthe ttp
s
known spectroscopic redshifts, drawn from different surveys (see F613Wband,arangeinwhichourmethodisnotabletoefficiently ://a
Table1),thefinalperformanceofresultingphoto-zswasevaluated. selectETs(seeSection4). ca
d
Theexpectedaccuracyforgalaxieswithmagnitudes<23.0inthe (iii) Magnitude selection. We selected only objects with mag- em
constructedF814Wnmbandis∼δz/(1+z)=0.011withasmall nitudes ≤23.0 in the F613W filter, and with magnitude errors ic
fraction of catastrophic outliers no higher than 3percent. In this <0.5. Above this magnitude limit, the reliability of signal-to- .ou
p
work,weusedtheBPZmeasuredphoto-zs,andthesamephotome- noise (S/N) measurements, photo-z estimations, and geometrical .co
trythatwasusedinphoto-zestimations.Allinformationaboutthe extended/point-sourceclassificationsdecreasesignificantly.Selec- m
photo-zmeasurementsandcataloguescanbefoundinMolinoetal. tionoftheF613WfilterisexplainedinSection3.2.2.Incomparison /mn
(2013). with previous criteria, magnitude selection is the most restrictive ras
2.1 Sampleselection odnite(ioi.vn)s,F5la7gpetersctesn.tInoftshoeurscaemspalrees‘egloecotdeddetthercotuiognhs’p(rSevEiXoTuRsACcToOnR- /article-ab
Wssaumerepcmaleer,rniftersde.eIonoutfthsitsthaseresmc,thiooarvnpi,hnwogleordegeliicsacabrlliecblepahtshsoeitfiodc-izafftaeionrendnoptnhseoalteowcmteieoltlnr-idccerifimtenereaida- F(bFaLLdAApGiGxep=lasr(a2Fm)L,e1At5eGrp=e=rc30e)),n,wt,2hp7illpuees<rbc1leepnnetdriacnregen,tphhoaasvvseiebcollytohsbeerlenFneLdiAgedhGbsovouaurlrusceoessr. stract/43
5
appliedtoobtainthefinalworkingsample. We classified morphologically all galaxies independently of their /4
FLAG parameter, including close/interacting systems. However, /34
(i) Extended–point-like source separation. To discriminate be- 4
thefinalstatistics(Sections5and6),andthepublishedcatalogue 4
tween the extended and point-like sources, we used the classifi- /1
(AppendixA)includeonlysourceswithFLAGvalues0and2(see 0
cation provided by Husillos et al. (in preparation). In all obser- Section4). 35
0
vations,asampleofmorethan2000real,bright,unsaturatedand 5
7
geometricallycircularpoint-likeALHsourceswasused,scaledto The final selected sample to be morphologicaly classified has b
y
lowerrandomfluxes,andinjectedintotheemptyregionsofopti- 43665galaxies. g
u
calimage.Afterthis,thesourceswererecovered,usingthesame es
parametrization as applied in source detection. Point-like sources t o
3 MORPHOLOGICAL CLASSIFICATION n
areconsideredassuccessfullyrecoveredwhendetectedatdistances 1
OF GALAXIES 1
lowerthan1arcsecfrompreviousinjectioncoordinates.Thesame A
p
procedurewasimplementedforextendedsources.Finally,thelo- 3.1 Generalmethodology ril 2
cus of point-like and extended sources was defined in the appar- 0
1
ent magnitude and surface brightness space (MAG_AUTO8 and The main tool used in this work to estimate morphologies is 9
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).
3448 M.Povic´ etal.
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Figure2. MagnitudeandredshiftnormalizeddistributionsoflocalsampleusedbyGALSVM(greensolidlines).Thissamplewasscaledinluminosityand ad
redshifted(blacksolidlines)tomatchthedistributionsoftheALHsample(reddashedlines).ShownexamplecorrespondstodistributionsintheALH-7field. ed
fro
m
h
in a real ALH background. Fig. 2 shows an example of redshift 3.2 GALSVMCONFIGURATIONANDCLASSIFICATION ttp
s
andmagnitudedistributionsoflocalsamplebeforeandafterbeing 3.2.1 Trainingsampleoflocalgalaxies ://a
redshifted and scaled in luminosity (green and black solid lines), c
a
andcorrespondingdistributionsoftheALH-7sample(reddashed We use a local sample of 3000 visually classified galaxies de
m
lines). (0.01≤z≤0.1)takenfromtheNair&Abraham(2010,hereafter ic
Wemeasuredsevenmorphologicalparametersonthissimulated N&A)cataloguewhichcontains∼14000galaxiesfromtheSDSS. .ou
dataset(andontheALHsamplelateron),andusethemsimulta- Thenumberofgalaxiesusedfortrainingisselectedasatrade-off p.c
o
neouslytotrainthevectormachine: betweenclassificationaccuracyandcomputingtime.Thecomput- m
/m
ingtimetotraintheSVMwiththecurrentalgorithmisindeedvery
n
(i) Ellipticity sensitive to the size of the training data set. We therefore chose ra
s
(ii) Abraham concentration index (CABR) – ratio between the objectsrandomlyandselectthesamenumberofET(ellipticaland /a
fluxesat30and90percentoftheradius(Abrahametal.1996) lenticular)andLT(spiralandirregular)galaxies,toavoidproblems rtic
le
(iii) Conselice–Bershady concentration index (CCON) – ratio related to unbalanced data sets, which is required for GALSVM to -a
b
betweencircularradiicontaining20and80percentofthetotalflux workproperly.Wecheckedthattherandomlyselectedsubsampleis s
(Bershady,Jangren&Conselice2000) representativeintermsofgeneralproperties(colours,magnitudes, trac
(iv) Gini (GINI)– cumulative distributionfunction of galaxy’s etc.)ofthecompletedataset.Fig.3showsthesecomparisonsfor t/4
3
pixelvalues(Abrahametal.2003) the g-band magnitude, redshift, g − r colour, N&A morphologi- 5/4
(v) Asymmetry(ASYM)–measuresthedegreeofasymmetryin calclassificationandinclination(forLTgalaxiesonly).Moreover, /3
4
thelightdistribution(Conseliceetal.2000) wealsocomparedmagnitudes,redshifts,andcoloursofETandLT 44
(vi) Smoothness(SMOOTH)–measurestherelevanceofsmall- galaxiesinboth,selectedandfullsamples.Ascanbeseenfromnor- /10
3
scalestructures(Conseliceetal.2000),and malizeddistributions,thepropertiesofrandomlyselectedandfull 5
0
(vii) M20momentoflight(M20)–fluxineachpixelmultiplied N&Asamplesareconsistentinallplots,asstatedbyKolmogorov– 57
bythesquareddistancetothecentreofthegalaxy,summedover Smirnov(hereafterKS)testswhichprovesthatnoselectionbiases by
the20percentbrightestpixelsofthegalaxy(Lotzetal.2004). areintroduced. gu
e
N&A classification was obtained in the SDSS g-band images, s
Even though several of these parameters appear to be redun- while our classification was carried out in the F613W band (cor- t on
dant, SVM were specially designed to be robust to redundancies respondstoSDSSrband;seeSection3.2.2).Takingintoaccount 11
in the feature space (see e.g. Huertas-Company et al. 2008). The thatweareperformingbroadclassification,separatingallsources Ap
set of parameters used to obtain the morphological classification intoETsandLTgalaxies,thedifferencebetweenthegandrbands ril 2
wastestedinPovic´etal.(2012).Mentionedparametersweremea- inspectedvisuallyisinsignificantforourwork.Foreachmorpho- 01
9
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
ALHAMBRA:morphologicalclassification 3449
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Figure3. ComparisonbetweenthepropertiesofthefullN&Asample(blacksolidlines)and3000galaxies(reddashedlines)selectedrandomlyfromthefull p
onetobeusedinourmorphologicalclassification.g-bandmagnitude(topleft),redshift(topright)andg−rcolour(middle)distributionsarerepresentedfor ril 2
0
allgalaxies(leftsubdiagram),ETs(middlesubdiagram)andLTs(rightsubdiagram).Wealsocomparedmorphologicaltypes(forallgalaxies;bottomleft)and 1
9
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.
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- D
o
fication, and excluding dwarf objects, while N&A galaxies range w
between0.01≤z≤0.1.Atthemoment,thisisthemostcomplete nlo
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localsamplethatcanbeusedtotestthemorphologyofhigh-redshift d
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agsatlraoxmieestrwici,thphthoetoGmALeStrViMc,creoddseh,iifnt,clmudoirnpghofloorgeiaccahltsyopuer,cseiztehepalirsatmo-f d from
eters,poststamps,masksandPSFimagesinallfiveSDSSbands. h
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3.2.2 GALSVMclassification mic
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ThefinalclassificationisperformedintheALHF613Wband,since u
p
theS/Nratioishigherinthisfilter,asshowninAparicio-Villegas .c
o
etal.(2010).ForeachGALSVMrun,wesetthequalityparametersthat m/m
correspondtousedALHimage:filtername,centralwavelength,full n
widthhalf-maximum,pixelscale,sigma,zero-pointandsaturation ras
l1teo.v0tewalr.ocGsbievrceo,nasedthemeTtaoybrppliehc1oa)lloraegnsidcoatlhuletciodlanespostefhst,h:weEeAThLaaHvnedsrueLrsTvte.riyFc(toemrdaeiiannclthyhiassrowouuorncrdke acFnuigdtsucuroseerr4de.sinpFoornoudmrimntgoorprpehtdooshlboiofgttistcoa(mrlic:glhdatsi)sstiirfinibctauhttieioonnA:sL≤oH2f-04F.06fi,1e≤3ldW21f.mo0r,as≤ginx2i1tmu.5da,eg≤sni2(tl2ue.df0te), /article-a
in the final selected sample (see Section 2.1), we compute seven ≤22.5and≤23.0. bstra
morphologicalparametersdescribedinSection3.1. c
Since the sample is fully dominated by faint objects given the 4 CALIBRATION OF THE MORPHOLOGICAL t/43
CLASSIFICATION USING COSMOS/HST DATA 5
shape of the magnitude counts (Fig. 4), when fainter objects are /4
included in the classification, the algorithm will be optimized to TheclassificationiscalibratedintheALH-4fieldusingtheclassi- /34
4
classifythesegalaxiesandthefractionofmisclassifiedbrightob- ficationofthesameobjectsobservedwithHST/AdvancedCamera 4
/1
jectsmightsignificantlyincrease.Toavoidthiseffect,weperformed forSurveys(ACS)intheCOSMOSsurvey.Themorphologicalclas- 0
3
themorphologicalclassificationwithsixincreasingmagnitudecuts: sificationinCOSMOSusedherewascarriedoutwithGALSVMby 50
≤20.0,≤21.0,≤21.5,≤22.0,≤22.5and≤23.0.Thatway,bright Huertas-Companyetal.(2009),andispubliclyavailablethroughthe 57
b
galaxies are classified using a training set made only of bright Tascaetal.(2009)catalogue.12 Theclassificationseparatesgalax- y
g
galaxies.Thesecutsalsocorrespondroughlytoincreasingredsh- iesintothreeclasses(early,lateandirregulars)butdoesnothave u
e
fits:≤0.7,≤0.9,≤1.0,≤1.2,≤1.2and≤1.3. probabilistic information though since it was done with an older st o
Thefinalprobabilityforeachgalaxyiscomputedastheaverage versionofGALSVM. n 1
oftheoutputprobabilityof15MCindependentruns.Again,this ByusingCOSMOSasthereferencesample,weexplicitlyneglect 1
A
numberisanempiricaltrade-offbetweencomputingtimeandac- the classification errors of the galaxies in COSMOS. This choice p
curacy,andisaresultofprevioustestscarriedoutwith10,15and is justified since we focus here on bright galaxies with only two ril 2
20runs.IneachMCrun,GALSVMselectsrandomlyabalancedset morphologicalclasses.Wealsoneglecttheeventualmorphological 01
9
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
ALHAMBRA:morphologicalclassification 3451
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Figure5. ComparisonbetweentheGALSVMclassificationintheALH-4andCOSMOSsurveysfordifferentF613Wmagnitudecuts:≤20.0(topleft),≤21.0 /m
(topright),≤21.5(middleleft),≤22.0(middleright),≤22.5(bottomleft)and≤23.0(bottomright).Ineachplot,wecomparethedistributionsofaveraged n
probabilities(PROB_AVG)measuredintheALH-4fieldforthreemorphologicaltypesclassifiedintheCOSMOSfieldusingtheHST/ACSimagingdata ras
(Huertas-Companyetal.2009;Tascaetal.2009):elliptical/S0(redsolidlines),spiral(bluedashedlines)andirregular(greendottedlines)galaxies. /artic
le
withGALSVMcode:E/S0(redsolidlines),spirals(bluedashedlines) Table2. p10thresholdforselectingETandLTgalaxies(p10sample) -ab
awnedloirorkegfuolratrhse(pgrroeebnabdiloittytedthrleinsehso)l.dF(oplElo,wpiLn)gtothaepspelyditsotrtihbeutAioLnHs, indifferentmagnitudebins. strac
classificationsothattheresultingclasstihficatthioncontainslessthan Mag.limit 20.0 21.0 21.5 22.0 22.5 23.0 t/43
5
d1ty0epfipene.erTctoheenthtfaoctlolponuwtarpimnogisnepa,atifrooanrmsaeftgreoirvsme:nthperonbeaibgihlibtyouthrirnegshmolodrpphtEho,lpotLghicwael pp11EL00 >>00..67 >>00..77 >>00..765 >>00..85 >–0.5 >–0.5 /4/3444/1
(i) Truepositives(tp):galaxieswithp >pE(p >pL)inALH 03
whichareclassifiedET(LT)inCOSMOES; th L th Hereafter,asampleclassifiedwithp10thresholdswewillcallp10 505
(ii) True negatives (tn): galaxies with pE<ptEh (pL<ptLh) in syasmeipnlet.hTehfiosllioswaisnagmspelcettihoantsw,aesrweleeaesxepilnaipnaipnerSaencdtiownh5ic.hHwoewaenvaelr-, 7 by
ALHwhichareclassifiedLT(ET)inCOSMOS; g
(iii) False positives (fp): galaxies with p >pE (p >pL) in inthesamewayweobtainedthep10thresholds,wemeasuredalso ue
ALHwhichareclassifiedLT(ET)inCOSMOES; th L th thethresholdsathighercontaminationlevels.Wecomparestatistics st o
(iv) False negatives (fn): galaxies with pE<ptEh (pL<ptLh) in isnouSreccetsiocnan5b,ewohbilteaintheedftuhlrlocuagthaldoigreucetocfonwtahcotl.e >44 000 selected n 11
ALHwhichareclassifiedET(LT)inCOSMOS. A
Each probability threshold, has a corresponding completeness, p
Thepurity(P,fractionofwell-classifiedobjectsamongallobjects which is reported later in Table 4. For the p10 sample, the com- ril 2
classifiedinagivenclass)andthecompleteness(C,fractionofwell- pleteness varies from ∼70percent for the brightest objects to 01
9
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
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.
D
5 MORPHOLOGICAL CLASSIFICATION o
w
IN ALH n
lo
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Aftercalibratingourmorphology,wethenrunintheconsistentway de
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stheettGhAeLGSVAMLSVcoMdceoinnfiwguhroalteioAnLfiHless,uarnvedyf.oIrnaslelvdeenteActLioHnsfiweledsm,ewae- from
suredmorphologicalparametersandaveragedprobabilities.Given h
the number of fields/observations and magnitude cuts (see Sec- ttps
tion3.2.2),weobtained288catalogues. ://a
c
Intotal,withGALSVMweobtainedtheclassificationfor85percent ad
e
ofselectedsources(withdifferentlevelsofcontamination),while m
15percentstayedunclassified.Sourcesforwhichwewereableto ic.o
measuremorphologicalparametershavedifferentlevelsofcontam- u
p
inationbyothermorphologicaltype.Weanalysedtheunclassified .co
m
sourcesintheALH-4field(overlapswithCOSMOS)byusingthe /m
HST/ACSimages,detectingthat∼80percentofsampleareinter- n
action/mergercandidates.Inaddition,∼40percentofunclassified ras
/a
Figure6. SampleofETgalaxiesindifferentmagnitudebins,observedin sSo/NurcinescowmepreardiseotencwteidthwthitehotthheerCtChrDee3,CwChDicsh(aisnaglreenaedryalmheanstlioowneedr rticle
theALH-4(topimages,F613Wband)andCOSMOS/HST(bottomimages, above).ThepopulationofET/LTsourceswemightbemissingfor -a
b
F814Wband)surveys. beingunclassifiedwithGALSVMisabout3percent. stra
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Figure7. SameasinFig.6,butforgalaxiesclassifiedasLT.
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/. D
Foreachmagnitudebinwethenappliedthep10 thresholds,ob- Wedonottreatmergersspecificallyinourclassification.How- ow
n
tained in the HST/ACS calibration phase to ensure a 10percent ever,wedominimizetheirpopulationbyexcludingfromourp10 lo
contaminationlevel(p10sample;seeTable2).Withthiscontami- classifiedsampleallsourceswithSEXTRACTORFLAGparameter>2 ade
noaftailolnc,lainssaifillefideslodusrwcees,kaesesphcolwasnsiifineTdab2l2e035(1tagbalelaaxlsieosp,r6o1vpideersctehnet (sienlcelcuteddesoabmjepcltes,wbuitthwceloesxecnluedigehdbtohuerms).frTohmeyaldloaneanltyesrisinprtheseetnotteadl d from
summaryonothersourcesclassifiedasET/LTwithhighercontam- inthispaper(seeSection4).Moreover,wesawthatmanyinteract- h
inationlevels).Ofthose,1640and10322sourcesareclassifiedas ingsystemsstayedunclassifiedwithGALSVM.Asmentionedabove, ttps
ET(photo-z<0.5)andLT(photo-z<1.0),respectively,downto ∼80percentofallunclassifiedsourcesshowclearsignsofdistor- ://a
magnitudes22.0intheF613Wband.Forthismagnitudelimit,the tions,closecompanions,areedge-onsystems,oramixture.Finally, ca
d
fractionofourETgalaxiesrespecttoLTsis∼16percent[consistent wecheckedtheHST/ACSimagesofallp10galaxiesthatoverlap em
withtheresultsobtainedbyHuertas-Companyetal.(2009)inthe withtheCOSMOSfield,inordertoquantifythepopulationofpos- ic
.o
COSMOSfieldusingthesamemethodofgalaxyclassification].In sibleinteractions.Fromindependentinspectionscarriedoutbyfive u
p
addition,formagnituderange22.0<F613W≤23.0weclassified persons,weobtainedtheaveragedpopulationof15.0±2.5percent .c
o
other10089LTgalaxieswiththesamelevelofcontaminationof merger/interactioncandidatesbetweenthegalaxiesclassifiedasLT. m
10percent and down to photo-zs of 1.3. For the rest of the sam- /mn
ple,thecontaminationishigher(includingalsoclassifiedsources ra
s
withveryclosecompanions(FLAG≥3)whichweexcludedfrom 5.1 Selectioneffects /a
thep10sample),asshowninTable3,beingingeneralaffectedby Sincethefinalcatalogueisobtainedafterapplyingarbitraryproba- rticle
theresolutionofourdataandbytheworstqualityofCCD3data. -a
bilitycuts,itmightbeaffectedbynon-trivialselectioneffectswhich b
Table 4 shows the number of objects and completeness of each weinvestigateinthefollowingfromthepointofviewofredshift stra
morphologicaltypeineachmagnitudebin.Besidestheinformation c
aboutp10sample,wealsoprovidethestatisticsathighercontam- andsizedistributions. t/43
5
ination levels. As expected, the level of contamination is directly (i) Redshift.Figs8and9showforeachanalysedmagnitudebin, /4
related with the magnitude, where fainter objects were classified the redshift distributions of ET and LT galaxies, respectively, of /3
4
4
withpoorerprobabilities.Athighercontaminationlevelswehave thefullsampleascomparedtothefinalp10 sampledefinedinthe 4/1
problemsclassifyingETsources,sincemanyLTsstarttomixwith previoussection.Foratotalsample(blacksolidlines),theclassi- 0
3
ETsgivingworstprobabilitydistributions.ForLTgalaxies,thecon- ficationisdirectlyobtainedfromtheTascaetal.(2009)catalogue 5
0
5
7
Table4. NumberandcompletenessofETandLTgalaxies,classifiedineachmagnitudebininrelationwiththecontaminationlevel.Besideeachnumberwe by
alsorepresentthepercentageofgalaxiesrespecttothetotalnumberofsourcesintheobservedcontaminationcategory(lastcolumn). g
u
e
s
F613Wmagnitude ≤20.0 20.0–21.0 21.0–21.5 21.5–22.0 22.0–22.5 22.5–23.0 t o
n
Contamination≤10percent 715(44percent) 549(33percent) 219(13percent) 157(9percent) – – 1640 11
C<10 0.7 0.4 0.4 0.3 – – Ap
Contamination≤20percent 1103(28percent) 1841(47percent) 622(16percent) 252(6percent) 112(3percent) – 3930 ril 2
ET C<20 0.7 0.4 0.4 0.3 0.1 – 01
9
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 – – – – – –