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ACCEPTED FOR PUBLICATION INAPJPreprint typeset using LATEX style emulateapj v. 04/21/05

THE STELLAR POPULATION OF Lyα EMITTING GALAXIES AT z ∼ 5.7

KAMSON LAI , JIA -SHENG HUANG, GIOVANNI FAZIOHarvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA

AND

LENNOX L. COWIE, ESTHERM. HU, YUKO KAKAZUInstitute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA

Accepted for publication in ApJ

ABSTRACTWe present a study of three Lyα emitting galaxies (LAEs), selected via a narrow-band survey in the GOODS

northern field, and spectroscopically confirmed to have redshifts of z ∼ 5.65. Using HST ACS and SpitzerIRAC data, we constrain the rest-frame UV-to-optical spectral energy distributions (SEDs) of the galaxies.Fitting stellar population synthesis models to the observed SEDs, we find best-fit stellar populations withmasses between∼ 109 − 1010 M⊙ and ages between∼ 5− 100 Myr, assuming a simple starburst star formationhistory. However, stellar populations as old as 700 Myr are admissible if a constant star formation rate model isconsidered. Very deep near-IR observations may help to narrow the range of allowed models by providing extraconstraints on the rest-frame UV spectral slope. Our narrow-band selected objects and other IRAC-detectedz ∼ 6 i′-dropout galaxies have similar 3.6µm magnitudes andz′ − [3.6] colors, suggesting that they possesstellar populations of similar masses and ages. This similarity may be the result of a selection bias, since theIRAC-detected LAEs andi′-dropouts probably only sample the bright end of the luminosity function. On theother hand, our LAEs have bluei′ − z′ colors compared to thei′-dropouts, and would have been missed bythe i′-dropout selection criterion. A better understanding of the overlap between the LAE and thei′-dropoutpopulations is necessary in order to constrain the properties of the overall high-redshift galaxy population, suchas the total stellar mass density atz ∼ 6.Subject headings: cosmology: observations — galaxies: evolution — galaxies:high-redshift

1. INTRODUCTION

The star formation history of high-redshift galaxies isan important problem in observational cosmology. It pro-vides tests for galaxy formation theories (e.g. Somervilleet al.2001; Springel & Hernquist 2003), constrains the sources ofreionization (e.g. Barkana & Loeb 2000, 2001), and mayeven shed light on the nature of the “first stars” (e.g.Bromm & Larson 2004).

Recently, the study of star formation at high redshift hasbeen catalyzed by the Infrared Array Camera (IRAC) onboard the Spitzer Space Telescope (Fazio et al. 2004). Theextraordinary sensitivity of this instrument has enabled thedetection of galaxies out toz ∼ 7. At such high redshifts,optical and near-IR observations accessible to ground basedtelescopes and the HST sample the rest-frame UV, which pro-vides important information on the on-going star formationand dust extinction. On the other hand, IRAC samples therest-frame optical light, which is less susceptible to dustex-tinction and is more representative of the emission from theless massive stars making up the bulk of the stellar popula-tion. Infrared observations are therefore indispensable whenstudying the star formation history of high-redshift galaxies.

Already, a sizable number of IRAC-detectedz ∼ 6galaxies have been discovered and studied in de-tail (Dow-Hygelund et al. 2005; Chary et al. 2005;Egami et al. 2005; Eyles et al. 2005; Mobasher et al. 2005;Schaerer & Pelló 2005; Yan et al. 2005; McLure et al. 2006;Yan et al. 2006). The infrared observations provided by IRAChas allowed the galaxies’ rest-frame UV-to-optical spectralenergy distributions (SEDs) to be constrained. Estimates of

Electronic address: klai@cfa.harvard.edu

the masses and ages of the galaxies’ stellar populations canthen be obtained. A stunning result from these studies is thatmany of these high-redshift galaxies are massive (& 1010

M⊙), and possess stellar populations that are older thanseveral hundred million years at a time when the age of theuniverse is less than 1 Gyr.

The vast majority of the IRAC-detectedz ∼ 6 galaxies stud-ied so far have been found using the Lyman break technique(Steidel et al. 1995, 1996, 2003) extended to high redshift(thei′-dropout technique; Bouwens et al. 2003; Stanway et al.2003; Yan & Windhorst 2004). Another, perhaps more effi-cient, method to search for high-redshift galaxies is to selectcandidates based on their Lyα emission (Rhoads & Malhotra2001; Ajiki et al. 2003; Hu et al. 2004; Malhotra & Rhoads2004; Taniguchi et al. 2005). The two methods suffer fromdifferent selection biases. For instance, while Lyα searchesmay allow discovery of sources not detected in the contin-uum (Fynbo et al. 2001), they may preferentially select younggalaxies in a dust-free environment (Malhotra & Rhoads2002). In a recent study, Gawiser et al. (2006) found that Lyαemitting galaxies atz ∼ 3 may be younger and less massivethan Lyman break galaxies at similar redshifts. It is thereforeinteresting to compare thez ∼ 6 Lyα selected galaxies to thei′-dropout sample.

In this paper, we present a study of threez ∼ 5.7 galaxiesdiscovered in the Great Observatories Origins Deep Survey(GOODS; Dickinson et al. 2003) northern field via a narrow-band Lyα survey. Using HST ACS and Spitzer IRAC datafrom GOODS, we study the rest-frame UV and optical prop-erties of Lyα emitting galaxies atz∼ 5.7. The goal is to derivestellar mass and age estimates, and to identify differences, ifany, compared toi′-dropout galaxies at similar redshifts.

2 Lai et al.

FIG. 1.— Images of the three IRAC-detectedz ∼ 5.65 objects ini′, z′, 3.6, and 4.5µm. Each panel is 10′′across, except for the zoomedz′ images, which are2′′across. The images are rotated from the original mosaics, when necessary, so that north is up. The ACS stamps are binned with a 3× 3 pixels box to bringout the faint extended sources. The full resolution unbinned stamps are shown in the zoomed panels. The circles indicatethe apertures used in the photometrymeasurements.

The paper is organized as follows. In § 2, we describethe candidate selection strategy and the photometry measure-ments. In § 3, we present results from population synthesismodeling of the observed SEDs. We compare our sample ofLyα emitting galaxies to thei′-dropout sample in § 4, andthe conclusions are presented in § 5. Hereafter, we will re-fer to the Lyα emitting galaxies as Lyα emitters (LAEs), andthe i′-dropout galaxies simply asi′-dropouts. All magnitudesquoted are in the AB magnitude system. We adopt a cos-mology of Ωm,ΩΛ,h = 0.3,0.7,0.7, consistent with therecent results from WMAP (Spergel et al. 2006).

2. CANDIDATE SELECTION AND DATA REDUCTION

2.1. Candidate Selection and Follow-up Spectroscopy

The high-redshift candidates are selected using a wide-fieldnarrow-band survey. At the time of this paper’s preparation,the survey is being expanded to allow for deeper and moreuniform selection criteria over a wider region. Details of thecompleted survey, selection criteria, and catalog will be pre-sented in an upcoming paper (Hu et al., in prep.). Here we willbriefly summarize the portion of the survey and the selectioncriteria used to construct the current sample.

The narrow-band survey, carried out using SuprimeCamon the Subaru Telescope, covers a 700 arcmin2 area encom-passing the GOODS northern field. This data set consists ofbroad-band continuumUVBRIz′ images (Capak et al. 2004;Hu et al., in prep.), supplemented by narrow-band observa-tions taken with the NB816 filter (Hu et al., in prep.). TheNB816 filter has a width of 120 Å FWHM and is centeredaround 8150 Å, corresponding to Lyα at z ∼ 5.7.

The narrow-band observations allow for the selection ofhigh-redshift candidates based on the objects’ narrow-band tobroad-band flux excess. A detailed discussion of the general

selection strategy forz ∼ 5.7 LAEs can be found in Hu et al.(2004), while considerations specific to this data set are dis-cussed in Hu et al. (in prep.). To summarize, objects withI − N > 0.7 are selected down to a narrow-band magnitudeof N = 25.5. Due to Lyα forest absorption and the galaxies’intrinsic continuum break, the high-redshift candidates mustalso satisfy either a)R − z′ > 1.8 and be undetected inB andV , or b) be undetected in all passbands redward ofI.

Candidates satisfying the selection criteria are followedupusing the DEIMOS spectrograph on the Keck Telescope. Atthe DEIMOS resolution, a number of candidates show sin-gle asymmetric broad lines, with long red tails and steep bluedrop-offs. Such asymmetric lines are characteristic of Lyαemission at high-redshift, and objects displaying such emis-sion lines are classified asz ∼ 5.7 LAEs.

Our initial sample of spectroscopically confirmedz ∼ 5.7LAEs consists of 20 objects (there are additional LAEs fromthe expanded survey region), with 12 of them falling insidethe 10′×16.′5 region where deep IRAC observations are avail-able from GOODS. Three objects, LAE#07, LAE#08, andLAE#34, show significant emission in the IRAC 3.6 and 4.5µm channels. From now on, our discussion will be focusedon these three IRAC-detectedz ∼ 5.7 LAEs. Spectroscopicredshifts of the objects are tabulated in Table 1. Coordinates,spectra, and Lyα properties for the objects will be presentedin an upcoming paper (Hu et al., in prep.) upon completion ofthe survey.

2.2. X-Ray Nondetection

In order to check for the possibility that our objects arehigh-redshift AGNs, we turn to the 2 Ms Chandra Deep FieldNorth (CDF-N; Alexander et al. 2003), in which all three ofour objects lie. We cross-checked our objects with the point-

Lyα Emitting Galaxies atz ∼ 5.7 3

FIG. 2.— Object images in the IRAC 3.6 and 4.5µm bands with theneighboring objects subtracted.

source catalog by Alexander et al. (2003) and found that noneof our objects is detected in that survey. The three IRAC-detected objects lie near the edge of CDF-N, where the sen-sitivity is lower at∼ 3× 10−16 erg cm−2 s−1 (3σ, full-band0.5 – 8 keV; see Fig. 18 in Alexander et al. 2003). Nonethe-less, a powerful AGN/quasar would have a rest-frame 0.5 –8 keV luminosity of around 1044 erg s−1 (Barger et al. 2003;Brandt & Hasinger 2005), which translates into an observed0.5 – 8 keV flux of 4× 10−16 erg cm−2 s−1 at z = 5.65 (as-suming a photon indexΓ = 1.8). Therefore, if our sourceswere x-ray luminous quasars we would expect to detect them.However, we cannot rule out the possibility of a weak AGNcontribution. On the other hand, Wang et al. (2004) surveyedusing Chandra a sample of 100 LAEs atz ∼ 4.5 found by theLarge Area Lyman Alpha (LALA) survey, and found no x-rayemission from any of the LAEs. Stacking analysis also revealsno detectable x-ray emission, placing a 3σ upper limit on theaverage 2 – 8 keV luminosity atL < 2.8×1042 erg s−1. Ourobjects are selected in a similar manner as the LALA LAEs,and so should be higher redshift counterparts of the LALAobjects. Therefore, given the objects’ nondetection in CDF-Nand the Wang et al. (2004) results, we consider it unlikely thatour objects are high-redshift AGNs.

2.3. Photometry

The optical photometry is measured from the v1.0 releaseof the HST ACS data from GOODS.1 A detailed descriptionof the GOODS ACS data is given in Giavalisco et al. (2004).To summarize, the northern field of GOODS covers an areaof ∼ 160 arcmin2 in four passbands: F435W (B), F606W (V ),F775W (i′) and F850LP (z′). The images have PSF FWHM∼ 0.′′12 and reach depths of 27.4, 27.5, 26.8, and 26.5 mag(3σ in 1′′-diameter apertures) in theB, V , i′, andz′bands, re-

spectively.Infrared coverage is provided by deep Spitzer IRAC obser-

vations, covering the same field as the ACS imaging, obtainedas part of the GOODS program (Dickinson et al., in prep.).2

The data consist of imaging in four passbands, centered at3.6, 4.5, 5.8 and 8.0µm. The IRAC photometry are mea-sured using images from our own independent reduction ofthe GOODS data. The 3σ magnitude limits (in 2.′′4-diameterapertures) in our images are 26.2, 26.4, 25.0, and 25.1 magin the 3.6, 4.5, 5.8, and 8.0µm bands, respectively. The PSFFWHM is ∼ 2′′ on average, but is slightly better at∼ 1.′′8 inthe 3.6 and 4.5µm bands.

The three objects are detected only in thei′, z′, 3.6µm, and4.5µm bands. Stamp images of the objects in these four pass-bands are shown in Figure 1. LAE#07 and LAE#08 appear tobe marginally resolved in the ACS images, with approximateangular sizes of∼ 0.′′7 and 0.′′35, respectively. Taken at facevalue, the angular sizes correspond to physical sizes of∼ 4and 2 kpc for LAE#07 and LAE#08.

We perform aperture photometry on the ACS images foreach of the three objects. Because of the extended natureof the objects in the ACS images, extra care has to be takenwhen selecting the appropriate aperture size. We adopt a 1′′-diameter aperture in the measurements. Curve of growth anal-ysis of the three objects suggests that. 1% of the total fluxfall outside an 1′′-diameter aperture. While a large apertureis not ideal for minimizing background noise contributions, itis necessary in order to measure the total flux of the objects.Since the fraction of enclosed flux within our chosen aper-ture is close to unity, and the objects are intrinsically faint andextended, we opted not to apply aperture corrections to thephotometry.

Photometry measurements on the IRAC images are consid-erably more challenging. At the IRAC resolution, all three ob-jects are unresolved and can be treated as point sources. How-ever, because of the large PSF, neighboring objects may sig-nificantly contaminate the photometry. For example, LAE#07is heavily blended with two other faint neighbors, whileLAE#08 is located in the wings of a bright foreground galaxy(Fig. 1).

To address this problem, we use a deblending techniquewhereby the contaminating neighbors are subtracted using thehigh resolution ACS images convolved with the IRAC PSF.We use the ACSz′-band image as input since it is the closestto the IRAC bands in wavelength and the objects are brightestin this passband. In fact, we in general use thez′-band imageas the reference for object positions. The IRAC PSF is ob-tained directly from the images by stacking∼ 10 bright pointsources within the field.

For each contaminating object in the vicinity of the target,thez′-band image is convolved with the IRAC PSF. Then, af-ter first subtracting all the neighboring sources using the cur-rent best-fits, the convolved image of the contaminating ob-ject is fitted to its IRAC counterpart. The parameters fittedfor are the object’s amplitude, background, and position. Thisprocess is iterated until a converged solution for every con-taminating object is found. Using the best-fit solution, thecontaminating objects are subtracted from the original IRACimage, leaving only the target. The result of this deblendingprocedure is shown in Figure 2. It is important to note that this

1 http://archive.stsci.edu/pub/hlsp/goods/v1/ h_goods_v1.0_rdm.html2 http://data.spitzer.caltech.edu/popular/goods/Documents/

goods_dataproducts.html

4 Lai et al.

TABLE 1MEASUREDFLUX DENSITIES

Redshift i′ z′ 3.6µm 4.5µm

LAE#07 5.635 0.071±0.047 0.135±0.030 0.53±0.20 0.41±0.17LAE#08 5.640 0.070±0.045 0.124±0.033 0.51±0.20 0.61±0.17LAE#34 5.671 0.073±0.043 0.091±0.025 0.31±0.20 0.28±0.17

NOTE. — Flux densities are given in units ofµJy. Quoted errors are the 1σuncertainties. In the IRAC bands, the errors include systematic uncertainties comingfrom confusion with neighboring sources. The objects are undetected inB, V , R,5.8µm, and 8.0µm, with 3σ upper limits of 0.042, 0.036, 0.063, 1.0, and 1.0µJy,respectively.

deblending procedure works best when the objects to be sub-tracted are unresolved, such as the neighbors of LAE#07. Theprocedure is less successful in subtracting the bright extendedforeground galaxy neighboring LAE#08, most likely due touncertainties and spatial variations in the IRAC PSF, and pos-sible wavelength dependence in the foreground galaxy’s mor-phology. Nonetheless, Figure 2 shows that the deblendingprocedure works quite well in removing contamination fromneighboring sources.

After the contaminating neighbors are subtracted from theIRAC images, we measure the objects’ photometry in 2.′′4-diameter apertures, centered at the positions derived fromthez′-band images. The aperture size is chosen to be as smallas possible to minimize background noise and residual fluxfrom nearby objects. Aperture corrections factors of∼ 0.5are applied to the measured fluxes.

Uncertainties in the ACS photometry are estimated using aMonte-Carlo procedure. Artificial point sources, with fluxesequal to the objects, are inserted into the images. The posi-tions of the artificial sources are assigned randomly apart fromthe requirement that they are at least 1 aperture diameter awayfrom detected sources. We then apply the same measurementprocedure we used on the objects to the artificial sources. Theresulting dispersion in the measured fluxes will serve as anestimate of the uncertainties in the objects’ photometry. Thisprocedure provides an estimate of the errors introduced by skyfluctuations and sky subtraction, and it takes into account thecorrelations between adjacent pixels introduced by the drizzleprocedure used to produce the ACS mosaics. The errors de-rived in this way also include the confusion noise due to faintundetected sources.

For the IRAC images, there is an additional source of errorcoming from the neighbor subtraction. Ideally, we would ap-ply a similar Monte-Carlo procedure, repeating the neighborsubtraction process many times on artificial sources to obtainthe error estimates. However, the convolution-based neighborsubtraction process is time-consuming, and so a full Monte-Carlo approach is impractical.

We therefore try to mimic the effects of neighbor subtrac-tion using the PSF-fitting package StarFinder (Diolaiti et al.2000). The errors in the source subtraction are dominated byimage quality issues, such as variations of the PSF across thefield, and by the properties of the individual sources (e.g. de-viations from being perfect point sources). The differences inthe source subtraction using StarFinder and our convolution-based procedure should be small compared to the aforemen-tioned uncertainties. Hence, we believe that performing thesource subtraction using StarFinder will give a reasonableestimate of the errors introduced by our convolution-basedneighbor subtraction process.

Using StarFinder, sources are detected and subtracted fromthe IRAC images. Artificial sources are inserted randomlyinto the source subtracted image and their fluxes are then mea-sured. Any errors arising from the imperfect source subtrac-tions, as well as the statistical errors coming from the back-ground and its subtraction, would be included in the resultingmeasured flux dispersion. The IRAC photometry errors thusderived are conservative since we expect our convolution-based subtraction to perform better than StarFinder in the casewhere the objects are not perfect point sources. Also, the arti-ficial sources are placed purely randomly throughout the im-age, so they may overlap subtracted sources. This tends tooverestimate the uncertainties since the errors in the subtrac-tion are larger near the center of the sources where the flux ishigher. The results of the flux measurements and error esti-mates described above are summarized in Table 1.

Finally, we supplement the ACS and IRAC imaging usingdata from the Hawaii-HDF-N project (Capak et al. 2004). Weattempt to measure the objects’ photometry in theR-band im-ages from Subaru and theHK′ images from the UH 2.2m tele-scope. However, these images are shallower than the ACSand IRAC data, and none of the objects are detected (in 3′′-diameter apertures) in eitherR or HK′. We include theR-bandupper limit (27.2 mag, 3σ) in our analysis for completeness.TheHK′-band upper limit (22.7 mag, 3σ) is too high to makea difference in the following analysis, and is therefore omit-ted.

3. SPECTRAL ENERGY DISTRIBUTION AND STELLARPOPULATION

3.1. Population Synthesis Modeling

After we have obtained reliable photometry for the galax-ies, we use the stellar population synthesis model ofBruzual & Charlot (2003, hereafter BC03) to model thegalaxies’ SEDs. The basic strategy is to calculate modelSEDs, varying the mass, age, dust reddening, metallicity, andstar formation history. The model SEDs are redshifted and in-tegrated through the filter response function. The model pre-dictions are then compared to the observed photometry andthe best-fitting model is found byχ2 minimization.

For simplicity, and to facilitate comparison with otherworks in the literature, we will assume throughout this studythe Salpeter (1955) initial mass function (IMF), implementedin the BC03 models with lower and upper mass cutoffs of 0.1and 100 M⊙. We will discuss the effects of using a differentIMF and other variations in the population synthesis modelsin § 3.3. We explore models with five different metallicities:Z/Z⊙ = 0.005, 0.02, 0.2, 0.4, and 1. Note that metallicity isnot treated as a free parameter in the fitting process, but ratheran input assumption of the model. The effect of dust red-

Lyα Emitting Galaxies atz ∼ 5.7 5

FIG. 3.— Best fit SSP models to the three IRAC-detectedz ∼ 5.7 LAEs.The blue-dotted (red-dashed) line is for a model withZ = Z⊙ (0.005 Z⊙).The observed SEDs are plotted as diamonds with error bars. The horizontalerror bars indicate approximately the FWHM of the passbands. When anobject is undetected in a certain passband, the corresponding 3σ upper limitis shown. Note that a 30% fractional error is added to thei′-band to accountfor the Lyα line contribution.

dening is included using the Calzetti et al. (2000) model, withE(B − V ) varying between 0.0 – 1.0. The effect of Lyα for-est attenuation is included using the model of Madau (1995).There is no need to fit for the objects’ redshifts, since theseare known from spectroscopy. An additional constraint aris-ing from the known redshifts is that the inferred ages shouldnot exceed the age of the universe, which is approximately0.99 Gyr atz ∼ 5.65. We therefore impose an upper limit of0.9 Gyr on the age of the best-fit models.

The star formation history (SFH) is parameterized by thetime evolution of the star formation rate (SFR). We considertwo SFHs in our analysis: Simple Stellar Population (SSP),i.e. instantaneous burst, and constant SFR. We choose not toconsider more complicated SFHs, such as an exponentiallydeclining SFR, because our ability to constrain extra parame-ters is limited. All our objects are detected in only four pass-bands, so we can constrain up to three parameters, which wechoose to be mass, age, and dust reddeningE(B −V ). Usingan exponential SFR would introduce an extra parameter, thestar formation timescaleτ , which can be constrained only ifwe fix the value of another parameter such asE(B − V). Wewill return to this point later in § 3.3. Note that the SFHs usedin our analysis (SSP and constant SFR) represent the two ex-tremes in star formation timescales, and an exponential SFRcan be thought of as an intermediate between these two exam-ples.

With two SFHs and five metallicities, there are 10 differ-ent classes of models in total. The best-fitting model withineach class is found byχ2 minimization. There are three freeparameters (mass, age, and reddening) and four data points

FIG. 4.— Best fit constant SFR models to the three IRAC-detectedz ∼ 5.7LAEs. Symbols and line styles have the same meaning as in Figure 3.

(i′, z′, 3.6µm, and 4.5µm), resulting in 1 degree of freedomin theχ2 fits. In practice, a commonly used measure of thegoodness of fit isχ2

ν, theχ2 per degree of freedom. When

ν = 1, a model is rejected at the 1, 2, and 3-σ levels if χ2ν

islarger than 1, 4, and 9, respectively. We in general considerafit to be acceptable if it givesχ2

ν< 4. In certain cases when

ν = 2 (see § 3.2), a fit is deemed acceptable ifχ2ν< 3.09 (the

corresponding 2σ level forν = 2).In order to make full use of the data available, we incor-

porate undetected data points as follows. Given the detectionlimit of the exposureσ and the model flux in the channelµ,we calculate the probabilityI = P(< σ|µ) that the actual fluxof the object is less thanσ. For the purpose of this calcula-tion, the flux is assumed to be normally distributed with meanµ and widthσ. For each undetected channel, a value of− ln(I)is then added to the totalχ2. In this way, if the model flux ismuch higher than the detection limit,µ≫ σ, I would be smalland the model would incur a largeχ2 penalty. For example,if the model flux in a certain channel is at the 3σ detectionlimit, µ = 3σ, theχ2 penalty would be 3.8. In practice, theundetected data points have only a small effect on the totalχ2, since the detection limits in most channels can easily besatisfied by reasonable models.

Since the Lyα emission line is located inside thei′-bandat z ∼ 6, and the BC03 model does not predict emission linestrengths, we have to take into account the possible Lyα lineemission when fitting to the data. The fractional contribu-tion of the line to the broad-band flux can be approximated asFL/FB = (R − 1)WN/WB, whereWN/WB andR = Fν(N)/Fν(B)are the ratios of the narrow-band to broad-band widths andflux densities, respectively. Our objects are selected basedon their narrow-band to broad-band flux excess, and typicallyhaveN − I ∼ −1.5. SubstitutingWN ≈ 120 Å andWB ≈ 1400

6 Lai et al.

FIG. 5.—∆χ2 contour for LAE#07 in the Z⊙ SSP model. The diamond

marks the best fitting model. The solid, long-dashed, and short-dashed linesrepresent the 1, 2, and 3-σ confidence regions.

Å for the NB816 andI filters, andR = 10−0.4(N−I) ≈ 4, we findthat the Lyα line may contribute around 30% of the totalI-band flux. Note that this approximation implicitly assumesthat the Lyα forest truncates similar fractions of the narrow-band and broad-band fluxes. Since Lyα at z ∼ 5.7 is locatedslightly redward of thei′-band center, Lyα forest truncationaffects thei′-band flux more severely thanI or NB816. TheLyα line contribution may therefore be substantially higherthan 30% in thei′-band. Nevertheless, we add an optimistic30% fractional error to thei′-band flux when we perform theχ2 fits.

3.2. Fitting Results

The observed SEDs of the three galaxies are shown in Fig-ure 3. Ignoring the lines for the moment, the first thing to takenote is that all three galaxies exhibit similar rest-frame UVand optical emission properties. This is not surprising sincethe three objects are selected from same data set using thesame selection criteria. The galaxies’ SEDs are characterizedby red UV to optical colors, with the flux increasing by morethan a factor of 3 fromz′ to 3.6µm. The SEDs also showsigns of flattening out beyond 3.6µm.

The best fit BC03 models to the observed SEDs are shownin Figure 3 and Figure 4 for the SSP and constant SFR mod-els, respectively. Only models with the two extreme metal-licities (Z⊙ and 0.005 Z⊙) are shown, even though fits wereperformed for all five possible metallicities. In practice,wefind good fits with competitiveχ2 for all five metallicities.Note that thei′-band flux is consistently higher than the mod-els, owing to the Lyα line emission. Also, since thei′-bandstraddles the Lyman-break, the model band-integrated flux islarger than it seems, and is usually within 1σ of the observedvalue.

The best-fit parameters differ depending on the SFH andmetallicity (Table 2). The best-fit Z⊙ SSP models to thethree LAEs have ages∼ 5 Myr, masses∼ 5× 109 M⊙, andE(B −V) ∼ 0.3− 0.4. The best-fit 0.005 Z⊙ SSP models favorslightly older ages (∼ 50 Myr), larger masses (∼ 1010 M⊙),and somewhat lower extinction. This is mostly because metal-poor stars produce more UV photons, so the best-fit modelsneed to be older in order to match the red UV-to-optical colors

FIG. 6.— Best fit SSP models to the three IRAC-detectedz ∼ 5.7 LAEswith fixed E(B −V ) = 0. Symbols and line styles have the same meaning asin Figure 3.

in the data. These results suggest that our sample ofz ∼ 5.7LAEs displays qualities of dusty young galaxies, seen imme-diately after or during a burst of star formation.

Compared to the SSP models, the constant SFR modelsin general yield older ages at& 700 Myr and slightly largermasses at a few times 1010 M⊙. In some cases, however, thebest-fit constant SFR models have similar parameter values asthe SSP models. This is not surprising since for young agesthe constant SFR model is similar to the SSP model.

Given that we only have four data points, some of whichhave fairly large error bars, it is important to explore the rangeof models allowed by the data. Furthermore, the somewhathigh levels of extinction we obtained may seem be a little sur-prising. Several previous studies have found little or no dustextinction in z ∼ 6 i′-dropouts (Dow-Hygelund et al. 2005;Eyles et al. 2005; Mobasher et al. 2005; Yan et al. 2005). Thefact that our galaxies are selected based on their Lyα emissionwould also lead one to expect low extinction values, since Lyαphotons are very susceptible to dust scattering (Charlot & Fall1991; Chen & Neufeld 1994; Hansen & Oh 2006). Do thedata permit models with low extinction? What is the rangeof age and mass allowed by the data?

To answer these questions, we calculate confidence regionsfor the best-fit parameters. In Figure 5, we show the∆χ2

contours for LAE#07 in the Z⊙ SSP model. It can be seenfrom the figure that firm upper limits on the age and masscan be placed on LAE#07. In the context of the SSP model,LAE#07 has a mass of. 3×1010 M⊙, and an age of. 300Myr. The contours and limits for the other two objects aresimilar.

One important thing to note in Figure 5 is that there is arange of models that fits the data. This is the result of a funda-mental degeneracy in the models: the red UV-to-optical colors

Lyα Emitting Galaxies atz ∼ 5.7 7

TABLE 2BEST-FIT MODEL PARAMETERS

Age [Myr] Mass [M⊙] E(B −V ) χ2ν

Age [Myr] Mass [M⊙] E(B −V ) χ2ν

SSP, Z⊙ SSP, 0.005 Z⊙LAE#07 4.8 2.4×109 0.275 2.05 60 1.7×1010 0.200 1.97LAE#08 3.2 5.0×109 0.425 2.19 30 2.9×1010 0.350 2.20LAE#34 4.4 1.4×109 0.275 2.42 80 9.1×109 0.150 2.36

Constant SFR, Z⊙ Constant SFR, 0.005 Z⊙LAE#07 5.0 4.5×109 0.375 2.14 720 2.6×1010 0.175 2.19LAE#08 4.8 6.9×109 0.425 2.19 720 3.9×1010 0.225 2.18LAE#34 720 1.1×1010 0.100 2.47 900 1.7×1010 0.150 2.37

SSP, Z⊙, No Extinction SSP, 0.005 Z⊙, No ExtinctionLAE#07 90 9.0×109 0.000 1.25 260 1.7×1010 0.000 1.05LAE#08 130 1.4×1010 0.000 1.45 320 2.3×1010 0.000 1.28LAE#34 80 5.1×109 0.000 1.41 230 9.2×109 0.000 1.27

of the observed SEDs can be satisfied by either a significantBalmer break (implying a relatively mature stellar popula-tion), or a young stellar population with high extinction. Fig-ure 5 shows that even though the best-fit model has a youngage and high extinction, there are models with lower extinc-tion values and older ages that will fit the data equally well.

Because of this degeneracy, we performed an alternative fitto the data forcingE(B −V ) = 0. The results of this fit for theSSP model are presented in Figure 6. Good fits to the datacan be obtained for all metallicities. The best-fit models tothe three galaxies in the Z⊙ case have masses∼ 1010 M⊙,and ages∼ 100 Myr. The 0.005 Z⊙ model gives older ages(around a few hundred Myr) and slightly larger masses. Ingeneral, the no extinction models have a lowerχ2

ν(see Ta-

ble 2), mostly due to the extra degree of freedom (ν = 2 inthis case). Note that in the SSP models presented here, therewill not be enough young massive stars around to producesignificant Lyα emission becuase of the older ages. The re-sults we obtained should therefore be regarded as a fit to theolder stellar population that dominates the total mass of thesystem. The observed Lyα emission can still be explained bythe presence of a young but significantly less massive stellarpopulation within the galaxy. We find no satisfactory constantSFR models withE(B−V ) = 0. Without dust extinction, thesemodels produce too much UV compared to the data.

In a recent study, Le Delliou et al. (2006) made detailedpredictions of the properties of LAEs, based on a hierarchi-cal galaxy formation model. We find that the best-fit stellarmasses of our LAEs are about 1 – 2 orders of magnitude largerthat the values predicted by Le Delliou et al. (2006). The mostprobable reason for this discrepancy is that a top heavy IMF isused in Le Delliou et al. (2006), resulting in a lower M/L ratioand hence lower overall mass. By requiring that our LAEs bedetected by IRAC, we may also be selecting galaxies fromthe high-mass end of the distribution (§ 4.2). Other prop-erties of the LAEs, such as the broad-band magnitudes as afunction Lyα flux, agree quite well with the predictions of theLe Delliou et al. (2006) model.

In summary, we find that the three IRAC-detectedz ∼ 5.7LAEs in our sample show similar emission properties and canbe fitted with similar model SEDs. The current data are unableto distinguish between models with different metallicities, asbest-fit models with competitiveχ2 can be found for all metal-licities. The observed SEDs are broadly consistent with twoclasses of models. In one model, the galaxies are dusty, young(ages. 100 Myr), and relatively less massive (M ∼ 109 M⊙).

In the other model, the galaxies are less dusty, more massive(M ∼ 1010 M⊙), and older (& 100 Myr) with a significantBalmer break. Both types of models can provide satisfactoryfits to the data. This degeneracy stems from the fact that weonly have two data points (i′ andz′) with which to measurethe UV spectral slope, which is sensitive to both dust extinc-tion and metallicity. Very deep near-IR observations in theJ,H, or K bands would provide valuable constraints on the UVspectral slope, and may help to break the degeneracy and ruleout some of the possible models.

3.3. Alternative Models

In addition to the basic models discussed in the previoussection, we also tried to fit a number of alternative models tothe data, in order to explore several effects not included inthe basic fits. These alternative models are discussed in turnbelow.

Exponential SFR. — We did not consider an exponentialSFR in our basic fits because with only four data points, itis not possible to fit for the mass, age, reddening, and starformation timescale (τ ) simultaneously. However, if we arewilling to sacrifice one degree of freedom in another param-eter, we can attempt to fit an exponential SFR model to thedata. In the previous section, we found that the galaxies canbe fitted by models with very little dust extinction. Otherstudies of galaxies at similar redshifts also found little or nodust extinction (Dow-Hygelund et al. 2005; Eyles et al. 2005;Mobasher et al. 2005; Yan et al. 2005). We therefore attemptto fit an exponential SFR model withE(B −V ) fixed at zero.Good fits to the data can be found using all metallicities, withτ on the order of 400 Myr. Since the best-fitτ is fairly large,the exponential model is not unlike the Constant SFR model.In fact, the two SFH models give similar masses and ages,which are∼ 1010 M⊙ and∼ 700 Myr, respectively.

Chabrier IMF. — In addition to the Salpeter IMF, theChabrier (2003) IMF is also implemented in the BC03 mod-els. The Chabrier IMF has a flatter distribution than Salpeterat< 1 M⊙. Stars more massive than 1 M⊙ therefore have alarger relative contribution to the total luminosity, resulting ina lowerM/L ratio. Hence, the Chabrier IMF in general pro-duces best-fit stellar masses that are∼ 30%− 40% lower thanthe Salpeter IMF.

Alternative Population Synthesis Model. — An alterna-tive population synthesis model was recently introduced byMaraston (2005). The main new ingredient of this model isan increased emphasis on the contributions of thermally pul-

8 Lai et al.

FIG. 7.— z′ − [3.6] vs. i′ − z′ colors of the LAEs in this paper and otherIRAC-detectedz ∼ 6 i′-dropouts in the literature. Only the error bars of theobject with the faintestz′ magnitude in each group are shown. This is done toavoid clutter and to provide a conservative estimate of the errors associatedwith each group of measurements. The vertical dotted line corresponds toi′ − z′ = 1.5, a typical selection criterion for thez ∼ 6 i′-dropouts. The curveshows the color evolution of a dust-free, 0.005 Z⊙, SSP mode. The curvecorresponds to model ages from∼ 30 Myr near the bottom of the plot to∼ 800 Myr near the top.

sating asymptotic giant branch (TP-AGB) stars to the overallluminosity. Contributions from TP-AGB stars are strongestin stellar populations around∼ 1 Gyr old, and their ef-fect is to raise the luminosity at near-IR and longer wave-lengths. In general, this results in a lowerM/L ratio, andhence smaller best-fit masses and ages (Mobasher et al. 2005;Maraston et al. 2006). However, since the LAEs in our sam-ple are in general much younger than 1 Gyr, and we havedata available only in the rest-frame optical and shorter wave-lengths, we do not expect the Maraston (2005) model to givesignificantly different results than the BC03 model. On theother hand, TP-AGB stars could potentially contribute to the5.8 and 8.0µm flux of z ∼ 6 galaxies, and we stress that it isimportant to investigate this alternative model in cases where5.8 and 8.0µm data are available.

4. COMPARISON TO THEi′-DROPOUT SAMPLE

One of the main goals of this study is to compare and con-trast the properties of thez ∼ 6 IRAC-detected LAEs andi′-dropouts. As we have mentioned in § 1, the different selec-tion criteria for LAEs andi′-dropouts may imply fundamen-tal physical differences between these two populations. Onething we try to accomplish is to compare the two populationsusing model-independent empirical indicators of the galaxies’properties, such as the rest-frame UV-to-optical color. Oursample of IRAC-detectedz ∼ 6 i′-dropouts is drawn from theworks of Dow-Hygelund et al. (2005), Eyles et al. (2005), andYan et al. (2005). Other objects are not included either be-cause they lie at higher redshifts (z & 6.5) so accuratei′ − z′

color cannot be measured owing to Lyα forest attenuation, orbecause IRAC photometry is not available.

4.1. i′ − z′ color

Thez′ − [3.6] color is plotted against thei′ − z′ color in Fig-ure 7 for both our LAEs and thez ∼ 6 i′-dropouts. The moststriking feature in Figure 7 is that the LAEs have much blueri′ − z′ color than thei′-dropouts. In fact, all three LAEs in oursample havei′ − z′ colors that are well blueward of the typi-cal i′-dropout selection criterion ofi′ − z′ > 1.5. One possiblereason for this difference is that thei′-dropouts are preferen-tially selected to have redi′−z′ color, which may also result inthei′-dropouts being selected at slightly higher redshifts than

FIG. 8.— IRAC 3.6µm magnitude vs.i′ −z′ color of the LAEs in this paperand other IRAC-detectedz ∼ 6 i′-dropouts in the literature.

the LAEs. Another reason is the unusually highi′-band fluxobserved in our sample (c.f. § 3.2), which may be explainedby the Lyα line contribution. We have remarked in § 3.1 thatthe Lyα line can contribute∼ 30% or more of the total broad-band flux. In addition, Hu et al. (2004) found that, in a sampleof LAEs selected in a similar manner as our objects, there isa clear correlation between the narrow-band andI-band mag-nitudes, and that all of the spectroscopically identified LAEsin the sample haveI − z′ < 1.5 (see their Fig. 5). Similarly, wefind that all three of the LAEs havei′ − z′ < 1.5.

The bluei′ − z′ colors of the LAEs in our sample impliesthat they would have been overlooked by previous surveysbased on thei′-dropout selection criterion. It is therefore im-portant to understand the overlap between the LAE andi′-dropout galaxies, so that the properties of the overallz ∼ 6galaxy population, such as the total stellar mass density, canbe constrained.

4.2. Stellar Population

At z ∼ 6, thez′ and IRAC 3.6µm bands sample the rest-frame UV and optical light, respectively. If we ignore theeffects of dust extinction, thez′ magnitude is sensitive to themassive young stars in the galaxy, while the 3.6µm magni-tude measures the contributions from less massive, evolvedstars. Thez′ − [3.6] color, plotted against thei′ − z′ color inFigure 7, is therefore an indicator of the age of the stellar pop-ulation. The model color evolution, also plotted in Figure 7,help to illustrate this point. The curve starts at∼ 30 Myr nearthe bottom of the plot. As the age increases,z′ − [3.6] alsoincreases, and eventually reachingz′ − [3.6] = 2.5 at an age of∼ 800 Myr.

As we have mentioned before, the presence of Lyα emis-sion may signal a young stellar population. However, Fig-ure 7 shows that there is no significant difference betweenthe z′ − [3.6] color of the LAEs and that of thei′-dropouts.Therefore, we find no evidence to suggest that there is a sys-tematic age difference between the IRAC-detected LAE andi′-dropout populations. However, we stress again that thez′ − [3.6] color is a good indicator of stellar population ageonly in the absence of dust.

Figure 8 shows the 3.6µm band magnitude plotted againsti′ − z′. All objects in Figure 8 lie at similar redshifts, andhence similar luminosity distances. This implies the 3.6µmmagnitude may serve as a measure of the total evolved stellarmass in the galaxies. The 3.6µm magnitudes of the LAEsfall comfortably inside the range of magnitudes observed forthei′-dropouts. Therefore, we again find no evidence that the

Lyα Emitting Galaxies atz ∼ 5.7 9

IRAC-detected LAEs andi′-dropouts are systematically dif-ferent.

In terms of stellar population synthesis modeling, the re-sults are consistent as well. For thez ∼ 6 i′-dropouts, stellarpopulation synthesis modeling in general gives ages aroundafew hundred Myr, and masses around 1010 M⊙. This is mostlyin line with the values we obtained for the LAEs (Table 2), atleast in the dust-free SSP case.

It is important to keep in mind the comparison presentedhere is restricted to the IRAC-detected LAEs andi′-dropouts.By requiring IRAC detection for the galaxies, the selectionis biased towards the bright end of the luminosity function.Recent studies ofi′-dropouts atz ∼ 6 suggest that the IRAC-invisible galaxies are in general younger and less massive(Eyles et al. 2006; Yan et al. 2006). Gawiser et al. (2006) alsofound that LAEs atz = 3.1 typically have lower masses at∼ 5×108 M⊙. The similarities seen in Figure 7 and Figure 8may therefore be a result of this IRAC-selection bias.

5. CONCLUSION

In this paper, we studied in detail the properties of threeLyα emitting galaxies, each spectroscopically confirmed tolie at z ∼ 5.65. Using ACS and IRAC data from GOODS, wemeasured the galaxies’ SEDs in the rest-frame UV throughoptical. Stellar population synthesis modeling then allows usto place constraints on the galaxies’ masses and ages. Ourmain conclusions may be summarized as follows.

The three IRAC-detected LAEs in our sample exhibit simi-lar emission properties, characterized by a red rest-frameUV-to-optical color, with the flux increasing by more than a fac-tor of 3 from z′ to 3.6 µm. This feature can be explainedby a young stellar population with significant dust extinction,or an older stellar population that has developed a Balmerbreak. Assuming the SSP model for the star formation his-tory, we find best-fit stellar populations with masses between∼ 109 − 1010 M⊙ and ages between∼ 5− 100 Myr. How-ever, stellar populations as old as 700 Myr are admissible ifa constant SFR model is considered. Very deep near-IR ob-servations may help to narrow the range of allowed modelsby providing extra constraints on the rest-frame UV spectral

slope. The available data provide very little constraints on theLAEs’ metallicity. We fitted stellar population synthesis mod-els using five metallicities ranging from 0.005 Z⊙ to Z⊙, andfound that equally good fits with similar parameter values canbe obtained for all metallicities.

In comparison with other IRAC-detectedz ∼ 6 galaxies se-lected based on thei′-dropout technique, we find that theLAEs and i′-dropouts possess similarz′ − [3.6] colors, sug-gesting that they are similar in ages. Also, the LAEs andi′-dropouts have comparable 3.6µm magnitudes, which implythey have similar masses. On the other hand, the compari-son is restricted to the IRAC-detected LAEs andi′-dropouts,which are likely to be the brightest and most massive mem-bers of their respective populations. The observed similaritiesbetween the IRAC-detected LAEs andi′-dropouts may be aresult of this selection bias.

Even though the IRAC-detected LAEs andi′-dropoutsshare some common characteristics, the LAEs have muchbluer i′ − z′ colors. Many previous searches forz ∼ 6 galax-ies were based on a combination ofi′ − z′ color selection andnon-detections in bands blueward ofi′. Because of their bluei′−z′ colors, LAEs would be overlooked by these searches un-less additional selection criteria are incorporated. One presentchallenge is therefore to understand the overlap between theLAE andi′-dropout populations. The solution to this problemwill shed light on thez ∼ 6 galaxy population in general, andhelp to constrain the total stellar mass density, as well as thetotal contribution of massive galaxies to the ionization back-ground atz ∼ 6.

We would like to thank the referee, Matt Malkan, for in-sightful comments that improved the paper. We also thankEric Gawiser and Haojing Yan for helpful discussions. EMHacknowledges suport from NSF grant AST06-87850 and LLCfrom AST04-07374. This work is based in part on observa-tions made with the Spitzer Space Telescope, which is op-erated by the Jet Propulsion Laboratory, California Instituteof Technology under a contract with NASA. Support for thiswork was provided by NASA.

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