Evolution of Balmer jump selected galaxies in the ALHAMBRA survey

14 Pages·2016·2.36 MB·English

A&A588,A132(2016) Astronomy
DOI:10.1051/0004-6361/201527552 &
(cid:2)c ESO2016 Astrophysics
Evolution of Balmer jump selected galaxies
(cid:2)
in the ALHAMBRA survey
P.TroncosoIribarren1,2,L.Infante1,2,N.Padilla1,2,I.Lacerna1,2,5,S.Garcia1,2,A.Orsi3,A.MuñozArancibia1,14,
J.Moustakas4,D.Cristóbal-Hornillos3,M.Moles3,6,A.Fernández-Soto9,10,V.J.Martínez9,11,12,M.Cerviño6,7,8,
E.J.Alfaro6,B.Ascaso13,15,P.Arnalte-Mur11,12,L.Nieves-Seoane10,11,andN.Benítez6
1 InstitutodeAstrofísica,PontificiaUniversidadCatólicadeChile,Avda.VicuñaMackenna4860,782-0436Macul,Santiago,Chile
e-mail:[email protected]
2 CentrodeAstro-Ingeniería,PontificiaUniversidadCatólicadeChile,Avda.VicuñaMackenna4860,782-0436Macul,Santiago,
Chile
3 CentrodeEstudiosdeFísicadelCosmosdeAragón,PlazadeSanJuan1,44001Teruel,Spain
4 DepartmentofPhysicsandAstronomy,SienaCollege,515LoudonRoad,Loudonville,NY12211,USA
5 MaxPlanckInstituteforAstronomy,Königstuhl17,69117Heidelberg,Germany
6 InstitutodeAstrofísicadeAndalucía(IAA-CSIC),GlorietadelaAstronomía,18008Granada,Spain
7 InstitutodeAstrofísicadeCanarias,VíaLácteas/n,38200LaLaguna,Tenerife,Spain
8 DepartamentodeAstrofísica,FacultaddeFísica,UniversidaddeLaLaguna,38206LaLaguna,Spain
9 UnidadAsociadaObservatorioAstronómico(IFCA-UV),46980Paterna,Spain
10 InstitutodeFísicadeCantabria(CSIC-UC),39005Santander,Spain
11 ObservatoriAstronòmic,UniversitatdeValència,C/CatedràticJoséBeltrán2,46980Paterna,Spain
12 Departamentd’AstronomiaiAstrofísica,UniversitatdeValència,46100Burjassot,Spain
13 GEPI,ObservatoiredeParis,CNRS,UniversitéParisDiderot,61,Avenuedel’Observatoire75014Paris,France
14 InstitutodeFísicayAstronomía,UniversidaddeValparaíso,Avda.GranBretaña1111,Valparaíso,Chile
15 APC,AstroParticuleetCosmologie,UniversitéParisDiderot,CNRS/IN2P3,CEA/lrfu,ObservatoiredeParis,
SorbonneParisCité,10rueAliceDomonetLéonieDuquet,75205ParisCedex13,France
Received13October2015/Accepted30January2016
ABSTRACT
Context. Samples of star-forming galaxies at different redshifts have been traditionally selected via color techniques. The
ALHAMBRA survey was designed to perform a uniform cosmic tomography of the Universe, and we here exploit it to trace the
evolutionofthesegalaxies.
Aims.OurobjectiveistousethehomogeneousopticalcoverageoftheALHAMBRAfiltersystemtoselectsamplesofstar-forming
galaxiesatdifferentepochsoftheUniverseandstudytheirproperties.
Methods.Wepresentanewcolor-selectiontechnique,basedonthemodelsofspectralevolutionconvolvedwiththeALHAMBRA
bandsandtheredshiftedpositionoftheBalmerjumptoselectstar-forminggalaxiesintheredshiftrange0.5<z<1.5.Thesegalaxies
aredubbedBalmer-jumpGalaxies(BJGs).WeappliedtheiSEDfitBayesianapproachtofiteachdetailedspectralenergydistribution
anddeterminedstar-formationrate(SFR),stellarmass,age,andabsolutemagnitudes.Themassofthehalosinwhichthesesamples
residewerefoundthroughaclusteringanalysis.
Results. Five volume-limited BJG subsamples with different mean redshifts are found to reside in halos of median
masses ∼1012.5±0.2M(cid:4) slightly increasing toward z = 0.5. This increment is similar to numerical simulations results, which sug-
geststhatwetracetheevolutionofanevolvingpopulationofhalosastheygrowtoreachamassof∼1012.7±0.1atz=0.5.Thelikely
progenitorsofoursamplesatz∼3areLyman-breakgalaxies,whichatz∼2wouldevolveintostar-formingBzKgalaxies,andtheir
descendantsinthelocalUniversearegalaxieswithluminositiesof1–3L∗.Hence,thisallowsustofollowtheputativeevolutionof
theSFR,stellarmass,andageofthesegalaxies.
Conclusions.Fromz ∼ 1.0toz ∼ 0.5,thestellarmassofthevolume-limitedBJGsampleschangesalmostnotatallwithredshift,
suggestingthatmajormergersplayaminorroleintheevolutionofthesegalaxies.TheSFRevolutionaccountsforthesmallvariations
ofstellarmass,suggestingthatstarformationandpossibleminormergersarethemainchannelsofmassassembly.
Keywords.Galaxy:evolution–Galaxy:halo–galaxies:high-redshift–galaxies:evolution–galaxies:general–galaxies:photometry
1. Introduction observational constraints have usually been summarized as
galaxyscalingrelationsthatmightormightnotchangewithred-
Most of the recent observational efforts to understand galaxy
shift (Mannucci et al. 2010; Elbaz et al. 2011; Bouwens et al.
evolutionhavebeenfocusedondeterminingthehistoryofcos-
2014; Troncoso et al. 2014), in high- or low-density environ-
mic star formation, gas density evolution, metallicity evolu-
ments, in extreme physical conditions (starburst, AGN galax-
tion, and mass growth of the Universe (Daddi et al. 2004;
ies), and in spatially resolveddata due to internalvariationsof
Mannucci et al. 2010; Madau & Dickinson 2014; Tomczak
the galaxy properties (Sanchez et al. 2013). In parallel, theo-
et al. 2014; Bouwens et al. 2015). These multiwavelength
retical works and simulations have tried to explain the physi-
(cid:2) BasedondataobtainedattheCalarAltoObservatory. cal mechanisms that reproduce the measured global properties
ArticlepublishedbyEDPSciences A132,page1of14
A&A588,A132(2016)
(Daddi et al. 2010; Davé et al. 2011; Lilly et al. 2013; Lagos Theyincludedourlackofknowledgeoftheprecisegalaxyred-
etal. 2014;Padilla et al. 2014).Despite these efforts, the com- shiftand selected the sample accordingto a certain probability
pletenessandcleannessofthesamplearestillchallengingprob- thresholddefinedbytheauthors.Therefore,thismethodbydef-
lems that depend on the sample selection-method, instrument initionselectsacleanbutnotacompletesample.
limits, and telescope time. These problems make the compari- In this work, we aim to develop a technique based purely
sonbetweenobservationalandtheoreticalworksevenmoredif- on photometric data to select star-forming galaxies. This type
ficult.Forexample,Campbelletal.(2014)comparedthestellar of selection allows us to directly compare our results with the
massofGALFORMgalaxiespredictedbythemodelwiththose previously mentioned works that also used a drop-out tech-
obtained through the fit of their predicted broad-band colors. nique to select their BzK, LBG, etc. samples. We use the
They foundthat both quantitiesdiffer for an individualgalaxy, uniform separation between two contiguous medium bands of
hencetheclusteringofmass-selectedsamplescanbeaffectedby the ALHAMBRA survey to register the redshifted position of
systematicbiases.Therefore,mass-selectedsamplesmightpro- the3646ÅBalmerjumpwithintheopticaldomain,allowingus
vide erroneousconclusionsregardingtheir progenitorsand de- toselectgalaxysamplesintheredshiftrange0.5<z<2.
scendants.Inaddition,theevolutionofscalingrelationsiscon- We based ourtwo-colorselection techniqueon theBruzual
strained with observations of galaxy samples that are selected & Charlot (2003) models and applied it to the GOLD
withluminosityorstellar-massthresholdsandarelocatedatdif- ALHAMBRA catalogs (Molino et al. 2014). In the following,
ferent redshifts, which does not necessarily constitute causally the galaxies selected by this method are dubbed Balmer-jump
connectedpopulations(i.e.,theydonotfollowa progenitor-to- Galaxies(BJGs), and theirphysicalpropertiesare investigated.
descendantrelation).Clustering-selectedsamplesovercomethis Basedonaclusteringstudy,wefindtheprogenitorsanddescen-
problembecauseinahierarchicalclusteringscenario,acorrela- dantsofgalaxiesinthesesamples,allowingustostudytheevo-
tionanalysisallowsustoestimatethebiasandhencestatistically lutionoftheproperties,derivedthroughaSEDfit,asafunction
determine the progenitors and descendants of galaxy samples. of redshift for halos of a certain mass. This paper is organized
The bias parametermeasuresthe clusteringdifference between asfollows:inSect.2wesummarizetheALHAMBRAobserva-
the galaxy spatial distribution and underlying dark-matter dis- tions and introduce the nomenclature of the ALHAMBRA fil-
tribution. Thus, it relates the typical mass of halos hosting the ter system used throughout the paper. In Sect. 3 we describe
galaxies(Shethetal.2001).Hence,measuringitingalaxysam- the selection method and justify it on the Bruzual & Charlot
ples at differentredshiftsdetermineswhether we are following (2003) models. The BJG samples are defined here. In Sect. 4
theevolutionofbaryonicprocessesoccurringinhalosofsimilar each galaxySED is modeled using iSEDfit(Moustakaset al.
massesornot.Thisfactisofextremeimportancebecauseonceit 2013), and the physical properties of each sample are charac-
isdetermined,wecanusethemultiwavelengthdatatostudythe terized as a whole. In Sect. 5 the clustering properties are cal-
evolutionofthebaryonicprocessesatcertainhalomass,estab- culated,and in Sect. 6 we discuss the main results. Finally, we
lishingadirectlinkbetweenobservationsandgalaxyformation concludeinSect.7.Throughoutthepaper,weuseastandardflat
models.Padillaetal.(2011)selectedearly-typegalaxiesaccord- cosmology with H = 100 hkms−1Mpc−1, Ω (z = 0) = 0.3,
0 m
ing to their clustering and luminosity function in the MUSYC ΩΛ(z = 0) = 0.7,σ8 = 0.824±0.029,andthe magnitudesare
survey.Sofar,nostudythatselectsstar-forminggalaxiesaccord- expressedintheABsystem.
ingtheirclusteringandluminosityfunctionhasbeenreported.
Star-forminggalaxiesareofparticularinterestbecausethey
2. Data:theALHAMBRA survey
allowustostudythemechanismsthatswitchthestarformation
onoroffanditsevolutionwithredshift.Consideringthelackof The Advanced Large Homogeneous Area Medium-Band
widespectroscopicsurveysinthesenseofwavelengthcoverage RedshiftAstronomical(ALHAMBRA1)surveyprovidesakind
andsurveyedarea,themajorityofthestar-forminggalaxysam- of cosmic tomography for the evolution of the contents of the
pleshavebeenchosenusingtwo-colorselectiontechniques.The Universeovermostofthe cosmichistory.Benitez etal. (2009)
so-called drop-out technique is based on recording the differ- have especially designed a new optical photometricsystem for
encebetweentwo distinctpartsofthespectrumthatgeneratea theALHAMBRAsurveythatmaximizesthenumberofobjects
breakonit(e.g.,the912ÅLymanbreakandthe3646ÅBalmer withanaccurateclassificationbySEDandphotometricredshift.
jump). This difference is strong enough that it has been mea- Itemploys20 contiguous,equal-width∼310Å, mediumbands
sured in broad bands, selecting star-forming galaxies at early
that cover the wavelength range from 3500 Å to 9700 Å, plus
periods of the Universe (z > 1.4), such as the BzK, BX, BM,
the standard JHK near-infraredbands. Moleset al. (2008)and
DOGs,andLBGs(Steideletal.1996;Daddietal.2004;Steidel
Aparicio Villegas et al. (2010) presented an extensive descrip-
etal.2004;Deyetal.2008;Infanteetal.2015).Severalauthors
tion of the survey and filter transmission curves. The observa-
havemeasuredthebias(Gawiseretal.2007;Blancetal.2008;
tionsweremadeatCalarAltoObservatory(CAHA,Spain)with
Guaitaetal.2010)bydeterminingthemassofthehaloinwhich
the3.5mtelescopeusingthetwowide-fieldimagersintheop-
each galaxy sample resides and by connecting the progenitors
tical(LAICA)andNIR(Omega-2000).Thetotalsurveyedarea
anddescendantsofthesegalaxysamples.Otherworksselected is2.8deg2 distributedineightfieldsthatoverlapareasofother
the samples by fully relying on their photometric redshift and
surveys such as SDSS, COSMOS, DEEP-2, and HDF-N. The
the physical properties determined through fitting the spectral
typical seeing of the optical images is 1.1arcsec, while for the
energy distribution (SED; Tomczak et al. 2014), far-IR lumi-
NIRimagesitis1.0arcsec.Fordetailsaboutthesurveyanddata
nosity (Rodighiero et al. 2011), or Bayesian approaches such
releasewerefertoMolinoetal.(2014)andCristóbal-Hornillos
as iSEDfit (Moustakas et al. 2013). Recently, Viironen et al. etal.(2009).We hereusedthepublicGOLDcatalogs2,which
(2015) implemented a method in the ALHAMBRA survey to
containsdataofsevenoutoftheeightALHAMBRAfields.The
select galaxy samples using the probability distribution of the magnitudelimits ofthese catalogsare (cid:6)m (cid:7) ∼ 25 forthe four
AB
photometric redshift (zPDF). The quality of the detailed SED
distribution,providedbythemediumbandsoftheALHAMBRA 1 http://www.alhambrasurvey.com
survey,allowedthemtoperformanaccuratestatisticalanalysis. 2 http://cosmo.iaa.es/content/ALHAMBRA-Gold-catalog
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Table1.NameandeffectivewavelengthofeachALHAMBRAfilter.
Name λeff Name λeff Name λeff
[nm] [nm] [nm]
U 365.5 U 396.5 B 427.5
1 2 3
B 458.5 B 489.5 B 520.5
4 5 6
B 551.5 R 582.5 R 613.5
7 8 9
R 644.5 R 675.5 R 706.5
10 11 12
I 737.5 I 768.5 I 799.5
13 14 15
I 830.5 z 861.5 z 892.5
16 17 18
z19 923.5 z20 954.5 Fig.1. ALHAMBRA filter transmission curve covering the optical to
NIR. From top to bottom, the red curves show the typical redshifted
Notes.Columns1,3,and5indicatetheadoptedfilternamethatisused spectrum of a star-forming galaxy at z ∼ 1.5, z ∼ 1.0 and z ∼ 0.5.
throughoutthepaper;Cols.2,4,and6showtheeffectivewavelengthof
ThegrayshadedareasmarkthepositionoftheALHAMBRAfiltersR ,
eachALHAMBRAband. 8
I ,andz ,whichhighlightsthepositionoftheBalmerjump.Toselect
14 20
theBJGsamples,weusethefilterdirectlyredwardofthegrayfilterto
sampletheendpositionoftheBalmerjump.
blue bands and from (cid:6)m (cid:7) ∼ 24.7 mag to 23.4 mag for the
AB
red bands. The NIR limits at AB magnitudesare J ≈ 24 mag,
H ≈ 23 mag, and K ≈ 22 mag. In the following, the fil-
ter nomenclature presented in Table 1 is used and the ALH-4 and showed that the data are complete until K = 20 or
VEGA
andALH-7fields are excludedfromour analysis.Previousau- K = 21.8. We have checked the K-band numbers counts
AB
thorshaveshownthatoverdensitiesresideinthesefields,which of the 48 individual pointing that comprise the ALHAMBRA
might alter the redshift distribution of the selected samples as GOLDcatalog.EverysinglepointingtendstofallatK ∼ 22.
AB
wellasthe clusteringmeasurements(see Sect. 5).Arnalte-Mur WeusedtheK =22limittodefineourcompletesamples.
AB
etal.(2014)obtainedtheclusteringpropertiesofALHAMBRA
galaxiesand studied the sample variance using the seven inde-
3.1.Selectionofstar-forminggalaxies
pendentALHAMBRAfields.Theyquantifiedtheimpactofin-
dividualfields on the final clustering measurements. Using the
WeusedthemodelsofBruzual&Charlot(2003)convolvedwith
Norberget al. (2011)method,they determinedtwo outliersre-
theALHAMBRAfiltersystemtodefineatwo-colorcriterionto
gions,whicharetheALH-4andALH-7fields.Inaddition,part
select star-forming galaxies analogously to the work of Daddi
oftheALH-4fieldspatiallycorrespondstotheCOSMOSfield.
etal.(2004).Theycreatedthe BzK color-selectiontechniqueto
Previous works have shown that this region is dominated by
cull star-forming galaxies at z > 1.4. This technique is based
somelarge-scalestructures(LSS),themostprominentarepeak-
on the Balmer jump, which is an indicator of recent star for-
ing at z ∼ 0.7, and 0.9 (Scoville et al. 2007). These structures
mation. They used the models of Bruzual & Charlot (2003) to
haveX-raycounterpartsandaprobabilityhigherthan30%ofbe-
identifythe redshiftedBalmer jumpin the z-band,creating the
ingLSS. Guzzoet al. (2007)studied theclusterslocated atthe color-selectioncriterion BzK ≡ (z−K) −(B−z) = −0.2;
center of the LSS (at z ∼ 0.7), while Finoguenovet al. (2007) where BzK ≥ −0.2 selects star-forminAgBgalaxies aAtBz > 1.4.
found diffuse X-ray emission in the most compact structures.
Ourapproachisanalogoustothe BzK methodinthesensethat
There are other LSS foundin the COSMOS field, butthey fall
we also used the Bruzual & Charlot (2003) models, but we
outoftheredshiftrangeofthesamplesstudiedinthispaper.
recordedredshifted Balmer jumpsin various(diverse)medium
bands to select galaxies at different redshifts. This situation is
illustratedinFig.1,whichshowsthetypicalspectrumofastar-
3. Sampleselection
forming galaxy redshifted to z ∼ 0.5, z ∼ 1.0 and z ∼ 1.5.
To exclude the stars from the original ALHAMBRA Gold The ALHAMBRA bands are overplotted from the optical to
catalog, we used the stellarity index given by SExtractor NIR ranges. We chose the U2-band to sample the region be-
(CLASS-STAR parameter C(K)) and the statistical star and fore the Balmer jump instead of the B-band. We selected the
galaxyseparation(Molinoetal.2014,Sect.3.6)encodedinthe U2bandbecauseitreachesahighercompletenesslevelthanU1.
variablestellarflag(Salh)ofthecatalogs.Throughoutthepresent ObjectsthatwerenotdetectedintheU2 bandwerealsoconsid-
paper, we define as galaxies those ALHAMBRA sources with eredinourselection,andtheirmagnitudelimitwasused.Redder
C(K) ≤ 0.8 andS ≤ 0.5.TheALHAMBRA Gold catalogis bandsmightsampleweakfeaturesbluewardoftheBalmerjump
alh
an F814W (i.e., almost an I-band) selected catalog. This band of low-redshift galaxies z < 0.4. The K band is exactly the
wascreatedbytheALHAMBRA teamasa linearcombination sameaswasusedbyDaddietal.(2004),whilethez-bandwas
ofALHAMBRAbands(seeEq.(5)inMolinoetal.2014),and replaced with the variable Xn-band, which covers the optical
itwasusedfortheirsourcedetection.Objectswithfaintfeatures range from 613 Å to 954 Å. This means that X can be any
n
in this band are notdetected,which mightaffectthe complete- ALHAMBRA filter from R to z (see Table 1). The X -band
9 20 n
ness of the selected sample. We discuss this in more detail in samplestheregiondirectlyredwardoftheBalmerjump,hence
Sects.4.3and6.1.Thiscatalogiscompleteupto F814W = 23 theU −X colorindicatesthegalaxyredshiftrangedepending
2 n
(Molinoetal.2014),henceweusedthislimittobuildourcom- ontheselectedn.Ontheotherhand,theX −K colorregisters
n
plete sample. Cristóbal-Hornilloset al. (2009) studied the NIR the duration of the star formation age. The different panels in
completeness using the early release of the first ALHAMBRA Figs.2and3showthetheoreticalevolution(Bruzual&Charlot
field. They determined a change in the slope of the K-band 2003) of the U X K ≡ (X − K)−(U − X ) color as a func-
2 n n 2 n
number counts in the magnitude range 18.0 < K < 20.0 tionofredshiftwith9 < n < 20(X = R ,....,z ).Red,green,
Vega n 9 20
A132,page3of14
A&A588,A132(2016)
Fig.2. Criteria for selecting star-forming galaxies. The solid and dashed lines show the color evolution of star-forming and passive galaxies,
respectively. Red,green,andbluedashed linesshow thepassivelyevolvingmodelswithaformationredshiftofz = 2,3,6,respectively.Red,
f
green,andbluesolidlinesshowmodelsthatevolvewithaconstantstar-formationrateforagesof0.2,1,and2Gyr.Thehorizontaldashedline
indicatesthecolorcutU X K ≡(U −X )−(X −K)=0.Ineachpanel,thestar-forminggalaxiesarelocatedabovethisthreshold.Theyellow
2 n 2 n n
shaded regions mark the redshift range of the selected BJG samples in each color combination. Dashed vertical linesindicate the redshift 0.5
and1.5.
andbluesolidlinesshowtheU X K-colorevolutionofconstant selectgalaxiesatredshiftz >0.5,0.6,0.7,0.8,0.85,0.95,1.05,
2 n
star-formationratemodelsforages0.2,1,and2Gyrandredden- 1.1,1.2,1.25,and 1.3.Ineach ALHAMBRA filter setU X K,
2 n
ingE(B−V)=0.3.Red,green,andbluedashedlinesshowthe this color condition selects star-forming and passive galaxies
U X K-colorevolutionofpassivelyevolvingmodelsforforma- in the wide redshiftrange.Theoretically,these selected passive
2 n
tionredshiftofz = 2,3and6.In eachpanel,the star-forming galaxiesalways lie at higher redshift(i.e., Δz > 0.25)than the
f
models always lie in the region U X K > 0. Consequently, to selected star-forming galaxies. To select galaxies in a narrow
2 n
select a sample at redshift higher than z ∼ 0.5, a combination redshiftrange,weusedmorethanonecolorcondition;firstthe
thatinvolvesaX filterredderthanR mustbeused,asweshow (U −X )−(X −K)>0conditionwithn= j,toselectallgalaxies
n 9 2 n n
inthebottompanelsofFigs.2and3. abovecertain redshiftz , and second we subtractedthe higher
j
redshiftsamples,selectedwithn≥ j+1(passiveandstarform-
3.2.BJGsamples ing),fromthefirstgalaxyselection.Forexample,ourlowestred-
shiftsamplewasselectedbyimposingtheconditionU R K =0
2 9
WeusedtheconditionU X K ≡(U −X )−(X −K)>0andthe (i.e.,selectgalaxiesatz > 0.5)andsubtractingthegalaxysam-
2 n 2 n n
homogeneouscoverageintheopticalrangeoftheALHAMBRA ples selected by U X K > 0 with n ≥ 10, which selects all
2 n
filter system, where n ranges from 9 to 20 (X = R ,..,z ), to galaxiesat z > 0.6. In this way, only the star-forminggalaxies
n 9 20
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Fig.3. Criteria for selecting star-forming galaxies. The solid and dashed lines show the color evolution of star-forming and passive galaxies,
respectively. Red,green,andbluedashed linesshow thepassivelyevolvingmodelswithaformationredshiftofz = 2,3,6,respectively.Red,
f
green,andbluesolidlinesshowmodelsthatevolvewithaconstantstar-formationrateforagesof0.2,1,and2Gyr.Thehorizontaldashedline
indicatesthecolorcutU X K ≡(U −X )−(X −K)=0.Ineachpanel,thestar-forminggalaxiesarelocatedabovethisthreshold.Theyellow
2 n 2 n n
shaded regions mark the redshift range of the selected BJG samples in each color combination. Dashed vertical linesindicate the redshift 0.5
and1.5.
in the redshift range 0.5 < z < 0.6 were selected (see yellow 4. PhysicalpropertiesoftheBJGsamples
shadedregionin Figs.2 and3).Followingthismethodandus-
In this section, we characterize each BJG sample by providing
ing the homogeneous separation between each ALHAMBRA
a mean characteristic value of its redshift, absolute magnitude,
band, we selected eleven star-forming galaxy samples peak-
ingatz ∼ 0.55,0.65,0.75,0.8,0.9,1.0,1.05,1.15,1,2,1.25,and stellarmass,age,starformationrate,etc.
z∼1.4bycullingthegalaxiesthatsatisfiedthestar-formingcri-
terionU X K ≡(U −X )−(X −K)>0,whereX =R ,...,z
2 n 2 n n n 9 19 4.1.Photometricredshifts
andcleanedof higherredshiftgalaxiesby subtractingthe sam-
plesthatsatisfiedU X K > 0,withn ≥ 10,...,20.Table2sum- WeusedtheBayesianphotometricredshift(zBPZ)publishedin
2 n
marizesthepropertiesoftheselectedsamplesusingthemethod the Gold ALHAMBRA catalogs (Molinoet al. 2014)to verify
describedabove.Weensuredthatallsamplesareroughlyinde- our selection method. The BPZ code was optimized to deter-
pendentofeachotherbyremovingthehigh-redshiftsamplesthat minephotometricredshifts,fordetailsonthezBPZcalculations
wereselected usingthe X filters withn ≥ j+1 (see Col. 3of see Molino et al. (2014) and Benitez et al. (2009). Figure 4
n
Table2)fromthesampleselectedwiththefilterXn=j. shows the zBPZ distribution of the eleven BJG samples. To
A132,page5of14
A&A588,A132(2016)
Table2.PropertiesoftheBJGselectedsamples. Table3.MedianphysicalpropertiesoftheBJGsamples.
Sample Initialset Cleansets N (cid:6)z(cid:7) Name N zBPZ (cid:6)M∗(cid:7) SFR Age
name U2XnK U2XnK Two-color M(cid:4) M(cid:4)yr−1 [Gyr]
BJG R n≥10 5489 0.5–0.6 BJG 5489 0.52+0.16 10.15+0.11 0.08+0.22 5.52+1.62
1 9 1 −0.14 −0.13 −0.23 −1.87
BJG2 R10 n≥11 5174 0.6–0.7 BJG2 5174 0.64−+00..1181 10.25−+00..1132 0.27−+00..2223 5.13−+11..7560
BJG3 R11 n≥12 4497 0.7–0.8 BJG3 4497 0.74−+00..1170 10.30−+00..1143 0.38−+00..2243 4.83−+11..6480
BJG4 I12 n≥13 4012 0.8–0.85 BJG4 4012 0.81−+00..1142 10.37−+00..1153 0.42−+00..2265 4.67−+11..6229
BJG5 I13 n≥14 3550 0.85–0.95 BJG5 3550 0.90−+00..1182 10.36−+00..1164 0.49−+00..2254 4.37−+11..5244
BJG6 I14 n≥15 2878 0.95–1.05 BJG6 2878 0.98−+00..1192 10.41−+00..1175 0.52−+00..2275 4.12−+11..4165
BJG7 I15 n≥16 2231 1.05–1.1 BJG7 2231 1.07−+00..2102 10.46−+00..1175 0.63−+00..2265 3.83−+11..4009
BJG8 I16 n≥17 2325 1.1–1.2 BJG 2325 1.15+0.12 10.45+0.16 0.69+0.26 3.63+1.05
BJG9 z17 n≥18 2058 1.2–1.25 BJG8 2058 1.24−+00..2114 10.45−+00..1198 0.72−+00..2275 3.41−+11..3061
BJG10 z18 n≥19 2140 1.25–1.3 BJG9 2140 1.29−+00..2251 10.38−+00..2109 0.72−+00..2254 3.16−+11..3025
BJG11 z19 n=20 3391 1.3–1.5 BJG10 3391 1.33−+00..3238 10.34−+00..2230 0.72−+00..2223 2.99−+11..2095
11 −0.42 −0.24 −0.21 −1.27
Notes.Column1,nameoftheselectedsample;Col.2,initialfilterset
Notes.Column1,nameoftheselectedsample;Col.2,numberofgalax-
usedforselection;Col.3,secondfiltersetsusedtosubtracthigherred-
iesselected;Col.3,BPZphotometricredshift;Col.4,logarithmofthe
shift galaxies from the initial sample; Col. 4, number of galaxies se-
stellarmass(ChabrierIMF);Col.5,logarithmofthestarformationrate;
lected;Col.5,expectedredshiftrangeofthesample.
Col.6,galaxyage.
BJG ,BJG ,BJG ,BJG , and BJG samples. The median of
2 4 6 8 10
zBPZ distribution, reported in Table 3, clearly agree with the
medianoftheexpectedredshiftrangeestimatedthroughthetwo-
colorselectioncriteriareportedinTable2.
4.2.SEDfitting
ForeachgalaxyoftheBJGsamples,wefitthe20opticalbands
plusthethreeNIRbandsusingtheBayesianSEDmodelingcode
iSEDfit (Moustakas et al. 2013). After we fixed the redshift
to the best-fit value given by BPZ in the ALHAMBRA cata-
log (Molino et al. 2014), iSEDfitcalculated the marginalized
posterior probability distributions for the physical parameters
in a certain model space that was previously defined. Using a
MonteCarlotechnique,wegenerated20,000modelSEDswith
delayed star formation histories SFH ∼ te−t/τ, where τ is the
star formationtimescale. The SEDs were computedemploying
the flexible stellar population synthesis models (FSPS, v 2.4;
Conroyetal.2009;Conroy&Gunn2010)thatarebasedonthe
miles
(Sanchez-Blázquezetal.2006)andBasel(Lejeuneetal.
1997,1998;Westeraetal.2002)stellarlibraries.Weassumeda
Chabrier(2003)initialmassfunctionfrom0.1−100 M(cid:4) andthe
time-dependentattenuationcurveofCharlot&Fall(2000).We
adopteduniformpriorsonstellarmetallicityZ/Z(cid:4) ∈ [0.04,1.0],
galaxy age t ∈ [0.01,age(z )] Gyr, rest-frame V-band at-
BPZ
tenuation A ∈ [0−3] mag, and star formation timescale τ ∈
V
[0.01,age(z )]Gyr,whereage(z )istheageoftheUniverse
Fig.4.Photometricredshiftdistributionoftheselectedsamples.Upper BPZ BPZ
ateachgalaxy’sphotometricredshift.
panel:red,pink,yellow,lightgreen,cyan,andbluehistogramsshowthe
zBPZ distribution of the BJG ,BJG ,BJG ,BJG ,BJG , and BJG Figures9and10showtheSEDsofagalaxyrandomlypicked
1 3 5 7 9 11
samples, respectively. Lower panel: magenta, orange, dark green, fromeachBJGsample.Thegreenfilledsymbolsshowthedata
green, and light blue histograms show the zBPZ distribution for the and their error bar. The model that minimize the χ2 is drawn
BJG2,BJG4,BJG6,BJG8, and BJG10 samples, respectively. The ver- with the red line. The universe of models, generated using the
tical colored lines show the median of the zBPZ distribution for the aforementionedsetup,and scaled by their reducedχ2 is shown
selectedsamples.Thelabellocateintheupper-rightcornerreportsthe
withtheblueshading.
medianredshift.
ForeachBJGsampleandcertainphysicalproperties,theme-
dian value of the sum of the posterior probability distributions
better visualize the distribution tails, the upper panel shows were calculated and are reported in Table 3. The uncertainties
the BPZ distribution of the BJG ,BJG ,BJG ,BJG ,BJG , indicatethe1σconfidencelevel;toaccountforasymmetricdis-
1 3 5 7 9
and BJG samples, while the lower panel presents the tributions,wedeterminedthepercentiles16and84.
11
A132,page6of14
P.TroncosoIribarrenetal.:TheevolutionofBalmerjumpselectedgalaxiesintheALHAMBRAsurvey
Table4.MedianphysicalpropertiesoftheBJGKsamplesconsidering
theabsolutemagnitudelimitK=−21.2.
Name N zBPZ (cid:6)M∗(cid:7) SFR Age
M(cid:4) M(cid:4)yr−1 [Gyr]
BJGK 3322 0.56+0.18 10.50+0.10 0.24+0.29 5.80+1.43
1 −0.14 −0.11 −0.30 −1.87
BJGK 3778 0.67+0.11 10.46+0.11 0.40+0.27 5.31+1.37
2 −0.16 −0.12 −0.26 −1.74
BJGK 3576 0.75+0.10 10.45+0.12 0.46+0.26 4.92+1.31
3 −0.12 −0.13 −0.26 −1.67
BJGK 3480 0.83+0.11 10.45+0.13 0.49+0.27 4.69+1.24
4 −0.09 −0.14 −0.28 −1.60
BJGK 3150 0.92+0.10 10.44+0.14 0.55+0.26 4.37+1.19
5 −0.12 −0.15 −0.26 −1.52
Notes.Column1,nameoftheselectedsample;Col.2,numberofgalax-
Fig.5.Absolutemagnitudeasafunctionofredshift.Thecoloreddots iesselectedfromtheBJGsamplesaftertheK-bandabsolutecut;Col.3,
representagalaxyofeachBJGsample,usingthesamecolorcodethan BPZphotometricredshift;Col.4,stellarmass(ChabrierIMF);Col.5,
in Fig. 4. The horizontal solid line shows the absolute magnitude cut starformationrate;Col.6,galaxyage.
K =−21.2.
abs
redefinethefirstfiveBJGsamplesinthefollowingbyselecting
4.3.ComparisonbetweenBJGsamples allgalaxiesbrighterthan K = −21.2.Furthermore,the BJG
abs n
samples with n ≥ 6 are not consideredin the following analy-
Inthissubsection,wecomparethepropertiesderivedfromSED
sis.Table4presentsthepropertiesofthefinalsampledefinition
fitting of the BJG samples for galaxies of similar K-band ab-
(BJGK ,withn < 6),whichtakesintoaccounttheabsolutelu-
solute magnitude, i.e. similar stellar mass. Therefore, in addi- minositny cut (K = −21.2). In Fig. 6 the comparison of the
tion to restricting the BJG samples within the magnitude sur- abs
physicalpropertiesof galaxieswith similar K-band luminosity
veylimit (see Sect. 3), we appliedan absolutemagnitudelimit
is shown. Red squares show the median value of the probabil-
for all samples. In the following, we determine the most ap-
itydistributionforeachBJGK sample(withn < 6),whilethe
propriateabsolutemagnitudelimitthatallowsustobuildthese n
coloredareasindicatetheirdispersion.Theerrorbarsshowthe
samples. Figure 5 shows the K-band absolute magnitude, ob-
medianerrorofthepropertiesderivedthroughtheiSEDfit.In
tainedthroughtheiSEDfit,asafunctionofBPZredshift.The
Sect.6.3theseresultsarediscussed.
red, orange, green, cyan, light blue, and blue dots indicate the
galaxiesintheBJG ,BJG ,BJG ,BJG ,BJG ,andBJG sam-
1 4 6 7 9 11
pleswithapparentmagnitudebrighterthanthemagnitudesurvey 5. Halomassesthroughclusteringanalysis
limitK =22,respectively.Wecannotethatpartlyasaresultof
Inhierarchicalclustering,structuresbuildupintimefromsmall
the Malquist bias, the farthest sample BJG is roughly com-
11
pleteonlyuntilK =−22.5(bluedots).However,bychoosing densityfluctuations.Smallstructuresagglomeratetobuildlarge
abs
structures. Dark matter halosare biased tracers of the underly-
this bright absolute limit to compare all BJG samples, we re-
ing matter density field. Massive halos lie in higher and rarer
strictourstudytothemostmassiveandbrightobjectsandalso
densitypeaksandare more clusteredthanlower masshalos. If
enormously reduce the statistics for each sample, especially at
agalaxypopulationishostedbyhalosofagivenmass,thenthe
low redshift. Hence, we decided to study all objects brighter
than K = −21.2, which corresponds to the absolute limit clusteringamplitudeof the galaxypopulation,comparedto the
abs
where the BJG sample at z ∼ 1 is complete and comparable, expecteddarkmatterclusteringatthesameredshift,canbeused
5
toderivethetypicalhalomasscorrespondingtothatgalaxypop-
in termsof K luminosity,to theotherlowerredshiftBJG
abs n<6
ulation.Thisisencapsulatedinthebiasparameter,b,definedas
samples.InFig.5theblacksolidlineshowstheabsolutemag-
nitudelimitK = −21.2.Wechosethez ∼ 1limitbecausethe b2 =ξgal(r)/ξDM(r),whereξgalandξDMarethetwo-pointspatial
abs
correlationfunctionsforgalaxiesanddarkmatter,respectively.
ALHAMBRAGolddataarean F814W (i.e.,almostan I-band)
selected catalog andthusare less sensitive to galaxiesat z ≥ 1 Thecorrelationfunctionatacertainredshift,ξ(r,z),canbechar-
withapronouncedBalmerjump.Forgalaxiesatz≥1,thespec- acterizedwithapowerlawandacorrelationlength,r0,through
ξ(r,z) = (r/r (z))γ,whereγisthepowerindex.Forsparsesam-
tralregiondirectlybluewardoftheBalmerjumpisbarelyornot 0
plesandatsmall scalesthis representationis typicallyused.In
at all detected in the F814W because the Balmer jump falls in
largersurveysandsimulationsthecorrelationfunctionisusually
the I band and the region directly blueward of it falls in the
13
modeledbycombiningtermsforgalaxiesinthesame haloand
R band.Since the F814W image detectionis a linear combi-
12 indifferentones(Zehavietal.2011;Contrerasetal.2013).
nationinvolvingtheR band,thesetypesofgalaxiesarebarely
12
Inthefollowing,wemeasurethebiasandmassesofthehalos
or notat all detected as sourcesin the final catalog.We expect
thathostthe BJGK samples.Specifically,foreach BJGK
a deeper selection of galaxieswith a pronouncedBalmer jump n<6 n<6
atz < 1.Hence,theBJGsamplesatz (cid:2) 1 selectedfrom Xn≤13 sample, we calculate the two-point angular correlation func-
tion, and then, using the Limbers deprojection, given a red-
bandsareoptimizedtobecompleteaccordingtotheF814Wse-
lection,whereasthesamplesatz ≥ 1areincomplete.Thelevel shift distribution and a cosmological model, we calculate the
otiffyinbceocmaupsleettehneersesmofaythbeeBmJGanny≥6effseacmtsplwesoriksindgiffitocgueltthtoerq(uea.gn.-, cbo2r(cid:13)relσa28ti,goanl(rl0e,nzg)t/hσ.28T,DhMe(bz)i,aswphaerraemσe8t,gearlf(oσl8lo,DwMs)fisrotmherr0oothtrmoueganh
theF814WbandisalinearcombinationofALHAMBRAbands square fluctuation amplitude in 8 h−1 Mpc spheres of galaxies
andbluewardregionsofthe Balmerjumpthatareundetectable (dark matter). The halo mass is obtained from the bias by us-
asaresultofintrinsicgalaxypropertiessuchasredshiftanddust ing the modelsof Sheth et al. (2001).We chose this procedure
excess). To minimize the incompletenessof our samplesin the and the 8 h−1 Mpc scale to directly compare our results with
comparisonof the physicalpropertiesat differentredshifts, we previous works that connected progenitors and descendants of
A132,page7of14
A&A588,A132(2016)
Fig.6.EvolutionofthephysicalpropertiesoftheBJGK samples.Thepanelsshowthemedianoftheprobabilitydistributionof(toptobottom
n<6
andlefttoright)thespecificstarformationrate,starformationrate,age, B-bandluminosity, K-bandluminosity,andstellarmass.Redsquares
show the evolution of BJGK galaxies brighter than K = −21.2. The colored areas represent the dispersion of each sample. The error bars
n<6
correspondtothemedianerrorofthephysicalpropertiesdeterminedbytheiSEDfit.DashedlinesshowthefittotheCANDELSanalysistaken
fromvanDokkumetal.(2013)forgalaxiesofstellarmassessimilartotheMilkyWay.
samples of star-forming galaxies (see Fig. 7, Adelberger et al. to calculate r we used the power-law approximation ξ(r,z) =
0
2005;Ouchiet al. 2005;Kashikawa et al. 2006;Gawiser et al. (r(z)/r (z))−γthatdependsonthespatialseparationandredshift.
0
2007;Hildebrandtetal.2007;Hayashietal. 2007;Blancetal. Likewise,thetwo-pointangularfunctionisw(θ,z)= A (z)θ(1−γ),
w
2008;Hartleyetal.2008;Yoshidaetal.2008;Guaitaetal.2010; where A (z) istheangularamplitude.By fittingthe correlation
w
McCracken et al. 2010; Lin et al. 2012). More detailed treat- function to this power law, between 0.005 and 0.2 degree, the
ments,suchasconsideringbothhalotermsanddifferentscales, A (z)isinferred.TheangularamplitudesarereportedinTable5.
w
arebeyondthescopeofthispaper. To calculate the spatial from the angularfunction,we used the
Limber (1953)inversion,which requiresa redshift distribution
N(z),andassumedacosmologicalmodel.Limber’sinversionin-
5.1.Angularcorrelationandcorrelationlength
volvessolvingthefollowingintegral(Kovacetal.2007):
To determine the angular correlation function, we used the (cid:2)
2
LandyandSzalay(1993)prescription rγ = (cid:2)Aw(c/H0)[ N(z)dz] , (2)
0 C F(z)D1−γ(z)N2(z)g (z)dz
w(θ)=(N −2N +N )/N , (1) γ θ d
gg gr rr rr
where N and N are the number of pairs at a separation θ wherethe cosmol(cid:3)ogyplaysa role in the Hubbleparameter H0,
of galaxigegs in thercratalog and points in a random catalog with gd(z) = (1 + z)2 1+Ωmz+ΩΛ[(1+z)−2−1], and in the an-
gular diameter distance D . The parameter C depends on the
the same layout as the galaxy sample, respectively. N is the θ γ
cross number of points between the galaxy and randomgr distri- powerindexsuchthatCγ =Γ(0.5)Γ[Γ0.[50(.γ5−γ]1)].Tobeconsistentin-
butions. For our calculationswe consideredfive ALHAMBRA ternally and compare our results with other works (Adelberger
fields(see Sect. 2) thatencompass36 pointingcatalogs.Errors et al. 2005;Ouchi et al. 2005;Kashikawa et al. 2006;Hayashi
were estimated by a jackknife method, which has been shown etal.2007;Hildebrandtetal.2007;Gawiseretal.2007;Blanc
to be a robust error estimator (Zehavi et al. 2011; Cabré et al. etal.2008;Hartleyetal.2008;Yoshidaetal.2008;Guaitaetal.
2007),althoughitcanoverestimatethevarianceonsmallscales 2010;McCrackenetal.2010;Linetal.2012),thevalueofγwas
(Norberg et al. 2009). We calculated the angular correlation fixedtothecanonicalγ=1.8.Thisvalueisfullyjustifiedbyex-
function 36 times, each time eliminating one catalog out of perimentswhereγwasleftfreeandisconsistentwiththecluster-
the36available.Theuncertaintywasestimatedasthevariance ingofluminosity-selectedALHAMBRAsamples(Arnalte-Mur
ofw(θ).WefollowedthemethodofInfante(1994),Quadrietal. etal.2014).F(z)accountsfortheredshiftevolutionofthecorre-
(2007)tocorrectfortheintegralconstraint.Asmentionedabove, lationfunction,whereF(z)=(1+z)−(3+(cid:10)),andweused(cid:10) =−1.2,
A132,page8of14
P.TroncosoIribarrenetal.:TheevolutionofBalmerjumpselectedgalaxiesintheALHAMBRAsurvey
Fig.7.Evolutionofthebiasfactor,calculatedwiththevarianceat8h−1 Mpc,forsamplesofstar-forminggalaxies.Reddiamondsindicatethe
BJGK samples. At z ∼ 2, the gray, blue, red, green, and light green squares show the sBzK samples of Hayashi et al. (2007), Blanc et al.
n<6
(2008),Hartleyetal.(2008),Linetal.(2012),andMcCrackenetal.(2010),respectively.YellowsquaresshowtheBMandBXselectedgalaxies
ofAdelbergeretal.(2005).Atz∼3,theblue,green,andredsquaresindicatetheLBGsselectedbyYoshidaetal.(2008),Kashikawaetal.(2006),
Hildebrandtetal.(2007),Ouchietal.(2005),respectively.GraysquaresshowtheLy-αemittersselectedbyGuaitaetal.(2010),Gawiseretal.
(2007).Thelinesshowthebiasevolutionfordifferenthalomasses(Shethetal.2001),whichareindicatedbeloweachlineattherightsideofthe
panel.
Table5.ClusteringpropertiesoftheBJGKsamples.
Name N (cid:6)z(cid:7) (cid:6)K (cid:7) A r Bias logM
gal abs w 0 h
BPZ mag 10−3 [h−1Mpc] (r=8h−1Mpc) [h−1M(cid:4)]
BJGK 3332 0.56+0.2 –22.3 4.2±0.2 4.67±0.33 1.36±0.09 12.71+0.13
1 −0.1 −0.15
BJGK 3778 0.67+0.1 –22.3 4.3±0.5 4.58±0.39 1.41±0.11 12.66+0.15
2 −0.2 −0.17
BJGK 3576 0.75+0.1 –22.4 4.0±0.2 3.79±0.25 1.24±0.07 12.27+0.14
3 −0.1 −0.16
BJGK 3480 0.83+0.1 –22.4 4.2±0.1 4.15±0.23 1.40±0.07 12.45+0.10
4 −0.1 −0.12
BJGK 3150 0.92+0.1 –22.5 3.7±0.2 4.01±0.23 1.40±0.07 12.37+0.11
5 −0.1 −0.12
Notes.Column1,nameoftheselectedsample;Col.2,numberofgalaxiesselectedfromtheBJGsamplesaftertheK-bandabsolutecutandimage
mask;Col.3,BPZphotometricredshift;Col.4,mediumK-bandabsolutemagnitude;Col.5,amplitudeofcorrelation;Col.6,correlationlength;
Col.7,biasfactorcalculatedwiththevarianceat8h−1Mpc.Column8,logarithmofthehalomassinunitsof[h−1M(cid:4)].
whichcorrespondstothevalueadoptedforaconstantclustering was taken from Peebles (1980) (Eq. (59.3)), where J is a
2
in comoving coordinates(Quadri et al. 2007). To calculate the parameter defined in terms of γ as J = 72/[(3 − γ)(4 −
2
correlationlengtherror,wetooktheerrorcontributionfromthe γ)(6−γ)2γ]. We fixed the spatial scale at 8 h−1 Mpc such that
amplitu√deoftheangularcorrelationfunction,thePoissonianer- ξgal(8,z) = (r0(z)/8h−1Mpc)γ.Theevolutionofthedarkmatter
rors(∼ N(z)), and the effects of the photometricredshifterror density variance in a comoving sphere of radius 8 h−1 Mpc is
inshiftingandbroadeningN(z)intoaccount. σ2 (8,z) = σ D(z), where D(z) is the linear growth factor at
DM 8
redshiftz.The biasmeasuredforeachBJGK sampleata scale
of8h−1MpcisreportedinTable5.
5.2.Biasandmassmeasurements
Figure 7 shows the bias factor as a function of redshift for
In turn we estimated the bias parameter, b, from the correla- theBJGKsamples(reddiamonds).Thelinesshowthebiasevo-
tion length. As pointed out above, b is related to r0 through lutionfordifferenthalomasses(Shethetal.2001),whicharein-
the spatial correlation function by b2 ≈ ξgal(r,z)/ξDM(r,z) or dicatedbeloweachlineattherightsideofthepanel.TheBJGK
b2 ≈ σ2 (r,z)/σ2 (r,z). The numeratorξ (r,z) ≈ J σ2 (r,z) samples approximately follows the bias evolution of halos of
gal DM gal 2 gal
A132,page9of14
A&A588,A132(2016)
oftheU andU ,whichreaches97%and99.7%,respectively.
1 2
Since U has a higher detection level than U , we tailored the
2 1
selectiontechniqueusingtheU band.
2
To select samples at z < 0.5, we tried to use filters bluer
than the R , whose central wavelength is lower than 613.5 Å.
9
Nevertheless, the theoretical evolution of the color U X K
2 n<9
of passive and star-forming galaxies, based on the Bruzual &
Charlot (2003) models, tends to occupy the same locus in the
color-redshift(U X K−z)plane.Hence,itdoesnotallowsep-
2 n<9
arating the star-formingfrom the passive galaxiesas clearly as
fortheU X K selectionwithn>9(seeFigs.2and3).
2 n
ThenumberdensityofeachBJGsampledecreaseswithred-
shift, probably because of the nature of these objects or be-
causeoftheMalquist-biasselectioneffectintheU -band,which
2
we didnotconsiderhere.Inaddition,we verifiedourselection
methodwith the BPZ photometricredshifts,whose uncertainty
increaseswithredshift,andthereforethenumberdensitiesmight
beunderestimatedaccordingtoδ (BPZ)=0.014z(BPZ).
z
Based on this color technique (visual inspection of Figs. 2
and 3), the redshift distributions should be narrower than in
Fig. 4. The distributions can become wider if the errors of the
photometricredshiftsareproperlytakenintoaccount.Themean
Fig.8. Halo mass as function of redshift for the BJGK samples.
n<6 formal BPZ errors of the BJG and BJG samples are 0.02
Squaresshowthemedianvaluesofthehalomass,whilepolygonsdraw 1 11
and 0.03, which corresponds to 20% and 30% of the total ex-
themasslimittakingintoaccounttheerrorofredshiftandbias.
pected width (≤0.1), respectively. We performed simulations
considering Gaussian redshift distributions filled randomly us-
masses∼1012.5h−1M(cid:4).Finally,wecalculatedthehalomassus- ing the same number of galaxies found in each sample, the
ingEq.(8)ofShethetal.(2001),whichrelatesthebiaswiththe expected width (0.1), and the formal BPZ error δz(BPZ) =
peakheight,ν=δ (z)/σ(M,z),whereδ isthecriticaloverden- 0.014(1+z(BPZ)).Byperturbingthephotometricredshiftwith
sc sc
sity computed using the sphericalcollapse model. Here we as- itscorrespondingerrorandrandomlychoosingapositiveorneg-
sumedδ =1.69.Then,thehalomassM wasobtainedthrough ativevariance,thewidthofthedistributionsincreasesbyafactor
sc h
σ(M,z)evaluatedattheredshiftsoftheBJGK sampleslistedin of 1.5 for the first BJG sample and up to twice this value for
1
Table4.InFig.8thehalomassoftheBJGKsamplesasafunc- theBJG11 samplewithincreasingredshift.Clearly,higherpho-
tion of redshift is shown. This is calculated with the variance tometric redshift errors increase the distribution width. In the
at8h−1Mpc.Squaresshowthemedianvaluesofthehalomass, AHAMBRAdata,BPZtendstolowerprecisionatI < 24.5AB
whilepolygonsshowa conservativelimit.Thisdrawsthemass (Molino et al. 2014). The global redshift distribution shows a
limitsconsideringthe1σdeviationoftheredshiftandbias.The meanof(cid:6)z(cid:7) = 0.86atthismagnitudelimit. Hencethedistribu-
resultsarepresentedinTable5. tionsatz≥0.9tendtobroadenevenmorebecausethephotomet-
ricredshifterrorsexceed0.014(1+z).Foradistributionatz∼1
withanoriginalwidthof0.1andδ (BPZ) = 0.03(1+z(BPZ)),
z
6. Discussion the width increases up to three times. Outliers that are due to
either photometricredshiftor the colorselection techniquecan
6.1.Selectiontechnique
alsocontributetothetailsandmayalsobroadenthephotometric
The new two-color selection technique U X K based on redshiftdistributions.
2 n
ALHAMBRA medium-bandsallows us to extract eleven sam- We also investigated the selection method by creating
ples of star-forming galaxies at z > 0.5 in narrower redshift an X band composed of two, three, and four consecutive
n
rangesthanwaspossibleinpreviousstudies(Daddietal.2004; ALHAMBRAbands.Thisincreasedthestatistics,accuracy,and
Steidel et al. 2004; Adelberger et al. 2005). This selection width of the redshiftdistributionof eachsample. Nevertheless,
method is based purely on the ALHAMBRA photometric data to fully exploitthe capabilitiesof a multi-mediumbandsurvey
and does not depend on the models assumptions, method, or inselectinggalaxiesinsmallredshiftranges,wechosetousea
templates used to determine the photometric redshifts or prop- uniquebandinourselectionmethod.
ertiesderivedfromSEDfit.Wevalidatedthistechniquewiththe Viironenetal.(2015)haveshownthatispossibletoobtaina
Bruzual&Charlot(2003)modelsandalsocheckedotherstellar cleangalaxysampleusingtheredshiftprobabilitydistributions,
populationsynthesismodels,whichgiveconsistentresults. which is by definition (intrinsically) not complete. The fact of
TheALHAMBRAGOLDdataareanF814W-selectedcata- choosingacertainprobabilitythresholdimpliesthatsomegalax-
log,thusitislesssensitivetotheregionsbluewardoftheBalmer ieswill notbeconsideredin thefinalsample.To overcomethe
jumpofgalaxiesatz>1.TheR bandonlycontributesto10% problemofpurityversuscompleteness,theysuggestedusingthe
12
ofthefinalF814Wdetectionimage,whiletheI ,andI bands information of the whole zPDF to select each sample. In this
13 14
contribute18%each.Hence,thedetectionintheF814W image workwetailoredandusedatwo-colorcriterionthatconsidered
becomespoorerforgalaxiesatz≥1withapronouncedBalmer alltheobservationaldataavailable,selectingacompletesample,
jump. butatthesame timewas probablymorecontaminatedthanthe
Toavoidcontaminationoflow-redshiftgalaxiesinoursam- samplesselectedaccordingtoredshiftprobabilitydistributions.
ples,weusedthebluestbandavailabletosampletheregionblue- We used the zBPZ-BJG distributions to calculate this contam-
wardoftheBalmerjump.Hence,westudiedthedetectionlevel ination by estimating how many objects have a BPZ redshift
A132,page10of14