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BzKs in GOODS-N : z~2 star forming galaxies Meger, Nicole 2008

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BzKs in GOODS-N:z 2 star forming galaxiesbyNicole MegerB.Sc. (Honours Physics and Astronomy) The University of British Columbia,2006A THESIS SUBMITTED IN PARTIAL FULFILMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinThe Faculty of Graduate Studies(Physics and Astronomy)The University Of British ColumbiaDecember 2008©Nicole Meger 2008AbstractThis thesis presents new deep near-infrared imaging data in one of the mostwell studied regions of the sky, the Great Observatories Origins Deep Survey’s northern field (GOODS-N). In particular, we have produced a science-grade Ks-band (2.2 [sm) source catalogue with a depth of KS(AB) = 23.8magnitudes. With our K-selected sample, we use the BzK two-colour selection criterion to find galaxies lying at redshifts between 1.4 and 2.5. Weinvestigate the redshift distribution of these BzKs using spectroscopic red-shifts as well as a new photometric redshift catalogue and find respectively,13% and 14% contamination from low redshift interlopers. We then compare the relationship between star formation rate and stellar mass with threedifferent star formation rate estimators, derived from the rest-frame ultraviolet, mid-infrared and radio properties. We obtain similar relationshipsfrom each of these methods and discuss the inherent uncertainty in estimating high redshift star formation rates. Finally, we test the BzK selectionmethod with two extreme z 2 galaxy populations. The BzK criterion isable to select submillimetre galaxies at redshifts from 1.4 to 2.5 even thoughthey have extreme star formation rates, and it can also effectively selecthighly dust obscured galaxies.IITable of ContentsAbstract iiTable of Contents iiiList of Tables vList of Figures viAcknowledgements vii1 Introduction1.1 Cosmology1.2 The star formation history of the Universe1.3 Observing z ‘ 2 galaxies1.4 Great Observatories Origins Deep Survey2 Data2.1 The Wide-field Infra-Red Camera2.2 Data Reduction2.3 Making the Source Catalogue2.4 Source counts2.5 GOODS-N multi-wavelength coverage2.5.1 Redshifts1414151611234667899103 BzK galaxy selection3.1 Photometry3.2 Matching to multi-wavelength3.3 Redshift distribution . .4 Star formation rate estimators . .4.1 Stellar mass4.1.1 K-band technique4.1.2 1.6,um stellar bump techniquedata21212122111Table of Contents4.2 Star formation rates 224.2.1 Ultra-violet 224.2.2 Mid-infrared 234.2.3 Radio 244.3 Comparing various SFR estimators 255 Comparing various z 2 galaxy populations 325.1 Submillimetre galaxies 325.2 Dust Obscured Galaxies 336 Conclusion 38Bibliography 40ivList of Tables3.1 GOODS-N multi-wavelength data 165.1 Submillimetre galaxies as BzKs 35VList of Figures1.1 Multi-wavelength depths of GOODS data 52.1 WIRCam filter transmission curves 112.2 K-band number counts 122.3 Comparison of new photometric redshifts with spectroscopicredshifts 133.1 Derivation of B, z and K aperture corrections 183.2 BzK diagram 193.3 Spectroscopic and photometric redshift distributions for BzKgalaxies 204.1 Calibration of estimating stellar mass from L1.6 264.2 Ultra-violet star formation rate vs. stellar mass 274.3 Mid-infrared star formation rate vs. stellar mass 284.4 Radio star formation rate vs. stellar mass 294.5 Stacking results for radio star formation rate vs. stellar mass 304.6 Comparison of multi-wavelength star formation rate relationships 315.1 Submillimetre galaxies on the BzK diagram 345.2 Dust obscured galaxy selection diagram 365.3 Dust obscured galaxies on the BzK diagram 37viAcknowledgementsI would like to thank my supervisor, Douglas Scott, for his advice, guidance,confidence and generous use of red ink. I have truly valued the opportunityto travel to various conferences, meetings and observing runs. I have alsoappreciated all the input and guidance I have received from Alexandra Popeincluding help with stellar mass estimates, infrared luminosity measurementsand our collaboration on SMGs and DOGs. I am grateful to Mark Dickinsonfor sharing his extensive knowledge of near-infrared observations and galaxyevolution. I would also like to thank Emanuele Daddi for passing alonghis BzK expertice, Lihwai Lin and Chi Hung Yan for their tireless workon the data reduction, Mark Brodwin for developing the new photometricredshift catalogue, Glenn Morrison for doing the radio stacking and LucSimard for obtaining the Ks-band observations. I must thank my family,friends and especially my officemates who have managed to keep me sane.And finally, David Slater who has been supportive to the bitter end and willbe immensely relieved when this is all over.viiChapter 1IntroductionThe very early Universe was extremely homogeneous and isotropic. Sincethen, matter has gathered into filaments and clumps forming a cosmic webof large scale structure (Bond et al., 1996). This structure is reproduced bysimulations of cold (non-relativistic) dark matter halos undergoing hierarchical merging due to gravitational attraction starting with initial densityperturbations which are of the sort produced in an early period of inflation(e.g. Peacock, 1999). The Universe is very well described by a model whichincludes radiation, normal baryonic matter, cold dark matter (CDM) andsome form of dark energy (often denoted by A). These components makeup the standard model of cosmology, ACDM.Visible matter appears to trace the distribution of dark matter on largescales, but the detailed picture of how the galaxies themselves form andevolve is not as well understood. Hierarchical merging of galaxies cannotfully describe the observed galaxy populations. Rather, we observe ‘downsizing’ (Cowie et al,, 1996) in the star formation history of the Universe:statistically speaking, massive galaxies form their stars earlier than smallgalaxies. Although the evolution of the collisionless dark matter is well understood, the complexities of ‘gastrophysics’ are much more challenging tomodel. As a result, theorists rely on simple prescriptions for assigning galaxyproperties to dark matter halos (so called ‘semi-analytic galaxy formation’).The range of length scales and physical processes involved means that it hasnot been possible to construct an ab initio calculation of how galaxies formand evolve.1.1 CosmologyThe Universe is expanding. The farther away a galaxy is, the faster itis moving away from us and the more redshifted the light from that galaxybecomes. For a given redshift (z), we can relate the emitted and the observedwavelength (A) and frequency (ii):1Chapter 1. Introduction1+z—=-. (1.1)em 1jobsSince more distant objects have higher redshifts and light travels at aconstant speed, we see distant objects at earlier times and observed redshiftis a monotonically decreasing function of time. In order to determine anobserved flux from the emitted luminosity we must redshift the wavelengthof the radiation but also include the effect of distance dimming through theluminosity distance (DL). This is a function of redshift and the parametersof the specific cosmological model:cfdzDL=(l+z)J(1.2)Here,A, 1mandrare respectively, the density (p) of dark energy, matterand radiation divided by the critical density(Pc =of the Universe andA+ lm + lr,while H0 is the present-day expansion rate of theUniverse. We use H0 = 73kms1Mpc,m0.3 andQA= 0.7 (Spergelet al., 2007).The emitted luminosity density can be computed via:VemLv 1’ObSLvOb$ = (47rD)vQbsSvb.(1.3)In this thesis, optical and near-infrared photometry are measured inmagnitudes. We use AB magnitudes except when explicitly stated (Oke,1974). We convert between AB magnitude and flux density (US) throughthe following equation:mAB= —2.5log(S{Wm2Hz’j)—56.1. (1.4)1.2 The star formation history of the UniverseWe would like to have a coherent picture of how the first collapsed halosand stars evolve into the types of galaxies we have in the Universe today.We can piece together an empirical picture of galaxy evolution by detectinglarge samples of distant galaxies and comparing how various physical properties change throughout cosmic time. Imaging and spectroscopy in differentwavebands allow us to determine characteristics such as morphology, stellarand dynamical mass. space density, nuclear activity and star formation rate.2Chapter 1. IntroductionThe popular Lilly-Madau plot (Lilly et al., 1996; Madau et al., 1996)tracks star formation density over time. Since its conception in 1996, manystudies have estimated the total star formation density at various redshifts(e.g. Baldry et al., 2005; Thompson et al., 2006; Reddy et al., 2008). For arecent Lilly-Madau plot, see figure 7 in Pérez-González et al. (2008), whichcompares results of several studies. Star formation measurements agreevery well up to z 1 but by z 2, the scatter significantly increases.High redshift galaxies are difficult to identify and once identified their starformation is complicated to measure. Using the BzK cirterion to selectz 2 galaxies and multi-wavelength data to determine their star formationrates will help determine the star formation history at higher redshifts.1.3 Observing z 2 galaxiesAt z ‘- 2, the locally well-understood, bright optical part of galaxy emissionis redshifted into the near-infrared (NIR). Powerful NIR imagers have onlyrecently become available, allowing for deep surveys to be carried out. Sincethese galaxies are often optically faint, we cannot rely on spectroscopy toidentify their redshifts. Instead, we use multi-wavelength data includingNIR for photometric selection approaches which can be tuned for particularredshift ranges (e.g. Steidel et al., 1996; Elston et al., 1988; Erb et al., 2003;Daddi et al., 2004).We count on photometry to not only select z 2 galaxies, but alsoto quantify their physical characteristics. Synthesizing multi-wavelengthdata gives an overall picture of the energy output of a galaxy, called thespectral energy distribution (SED). SEDs can be modelled by inputtingmany parameters, such as initial mass function, metallicity, stellar mass,star formation rate, star formation history and dust content (Bruzual &Charlot, 2003). The initial mass function (IMF) describes the distribution ofstellar masses which are formed. For our work, we use the standard SalpeterIMF (Salpeter, 1955). The metallicity is a measure of the fractional mass ofelements heavier than helium. Stellar mass is dominated by old stars and soto estimate it we must measure the amount of light coming from 1.6 im inthe rest-frame (Sawicki, 2002). To quantify the current star formation rate(SFR), we need to measure the amount of light coming from young stars.Disentangling how much light comes from young stars relative to the oldstars gives a sense of the star formation history which can be determinedmore comprehensively by synthesizing spectra coming from stars of varyingages (Bruzual & Charlot, 2003). Dust complicates these measurements since3Chapter 1. Introductionit absorbs optical and UV radiation and re-emits in the infrared.We calibrate relationships to convert between flux density and star formation rate using local galaxies which can be observed in great detail (e.g.Caizetti et al., 2000). However, there are complications in applying locallycalibrated relationships to distant galaxies. For example, IMF and dustextinction both depend on metallicity, which is usually not very well constrained in distant galaxies (Caizetti, 2008a). One way to understand howreliable these estimates are is to compare various methods of determiningthe same characteristic, such as SFR, and attempt to understand their differences. Thus, we should be able to understand whether there are simpleand effective methods of estimating SFR directly from photometry.1.4 Great Observatories Origins Deep SurveyThe Great Observatories Origins Deep Survey (GOODS; Giavalisco et al.,2004) was conceived to focus the efforts of NASA’s three major space telescopes (HST, Charidra and Spitzer) on the same two areas of the sky toproduce the best wavelength coverage possible. The two fields were chosento be quite unremarkable and therefore, presumably representative of thedeep Universe in general. To enable follow-up by all the world’s telescopes,one field was chosen in each of the two hemispheres. The northern field selected is the Hubble Deep Field North (HDFN) and the southern field is theChandra Deep Field South (CDFS), GOODS-N and GOODS-S respectively.GOODS-N is a 10’ x 16’ field centred at12h36m55s,+62°14’15” (Giavaliscoet al., 2004). Since the project’s conception, the GOODS fields have alsobeen targeted by many ground-based telescopes.This thesis focuses on new observations in GOODS-N at near-infrared(NIR) wavelengths. Prior to our data from WIRCam on CFHT (discussedat length in chapter 2), the best available NIR data on GOODS-N werefrom FLAMINGOS on KPNO (Elston et al., 2003), which are significantlyshallower than the NIR data on GOODS-S from ISAAC on the VLT (Moorwood et al., 1998). Figure 1.1 shows the NIR depths compared to the multiwavelength sensitivities of the GOODS-N optical data from both HST andSubaru and the infra-red data from Spitzer. The GOODS-N NIR data arenow comparable to that of GOODS-S.4Chapter 1. IntroductionI111111I I22KPNO/FLAMINGOSxWang et al. UH88”X —••24CFHT/WIRCAMGOODS—S: VLT/ISAACH00 026 Subaru+KPNO.I-0oSpitzer/IRAC028HST/ACSI ililil I liii0.2 0.4 0.6 0.8 1 2 4 6 8 10wavelength (urn)Figure 1.1: Multi-wavelength sensitivities of GOODS data. Solid redsquares show current GOODS-N depths. Open squares show expecteddepths from proposed 2009A observations for H- and J-band and with theinclusion of UH data to become publicly available for K-band.5Chapter 2DataThis thesis was motivated by the efforts of the GOODS collaboration to fill inthe near-infrared (NIR) gap in the multi-wavelength coverage for GOODS-N. This chapter describes the NIR camera we used, the data reduction andcatalogue extraction processes and finally summarizes the multi-wavelengthdata available in GOODS-N.2.1 The Wide-field Infra-Red CameraThe Wide-field Infra-Red Camera (WIRCam) (Puget et al., 2004) ontheCanada France Hawaii Telescope (CFHT) is a NIR imager which begantaking data in the 2006A semester. WIRCam has J, H and K broadband NIR filters shown in figure 2.1, centred at 1.25, 1.65 and2.l5LLm. Thecamera has a 21.5’ x 21.5’ field-of-view and is fully-sampled at 0.3” per pixel.The camera includes four 2048 x 2048 HAWAII2-RG CCDs (referred to as‘chips’ hereafter). The chips have 45” gaps between them, but ditheringallows for a nearly uniform exposure time over the whole area.In 2006A, members of the GOODS collaboration attempted to fill theNIR gap in the multi-wavelength coverage of GOODS-N. A joint effort submitted proposals to both the Canadian and Taiwanese agencies by P.1’sLuc Simard and Lihwai Lin, respectively. Canadian time was awarded forobservations in the Ks-band (06AC33) while Taiwanese time was used forthe J-band (06AT02). In 2006A a Hawaiian group (P.1. Len Cowie) alsoobtained Ks-band observations (06AH45 and 06AH96) in GOODS-N, whichwere released at the end of February 2008 after an extended proprietaryperiod due to delays at Terapix affecting all WIRCam data.The Hawaiiangroup continues to receive time to observe the field in K; their 2007A data(07AH36) are scheduled to be released in February, 2009, whiletheir 2008Adata (08AH25) will be released in August, 2009. Our Taiwanese collaborators received more time to observe in J in 2007A (07AT18 and 07AT19).This thesis will focus on the K (referred to as simply “K” hereafter)data, though the reduction effort has been carried out in parallel for bothK and J.6Chapter 2. Data2.2 Data ReductionIn the first stack that we received, data were pre-processed at CFHT by the‘Piwi pipeline developed by Loic Albert for WIRCam data. The individualimages were then sent to Terapix in France to be stacked and distributed.A hardware issue caused early WIRCam observations to have cross-talkartifacts appearing as a repeating pattern (often donut-shaped) above andbelow every bright source. The first stacks received from Terapix also hadlarge scale background gradients and bright star-tracks. The astrometrywas quite good in the central GOODS-N region, but the extended regionhad significant astrometric residuals (as large as 1”). For early analysis,we simply masked out large regions of the image around bright objects andworked in the central GOODS-N region. In the first such reduction, wereached a 5cr depth of K(AB) = 23.5 with 10.4 hours of integration time.Our collaborators in Taiwan then took over data reduction in an attemptto improve on the Terapix release. Lihwai Lin and Chi-Hung Yan workedwith Wei Hao Wang to develop a reduction script to reduce crosstalk. Thisprogram finds the brightest sources and then removes excess signal fromevery 16 pixels above and below each source. The 16 pixel offset correspondsto the fact that after reading out, the detector is reset in 16 line blocks.During the stacking process, individual frames are transformed by matching to 2MASS sources (Skrutskie et al., 2006). Since GOODS-N is well outof the Galactic Plane and saturates for the brightest stars, there are onlyabout 100 stars over the whole field which can be used to make the transformation. Using the SDSS catalogue (York et al., 2000) instead of 2MASSsignificantly reduces the mean astrometric offset. Although there are stillsome large residuals in the lower right corner of the image which we donot understand, the majority of the image has very accurate astrometry(< 0.15”). It is possible that there are slight differences between each of thefour chips on the detector, explaining why one corner of the image is notas consistent. Small chip effects are also seen in the photometry but theseeffects are small (typically below icr) and can be ignored.After crosstalk removal and pre-processing with ‘I’iwi, stacking is donewith SCAMP and SWARP developed by Terapix. We have now settled ona reduction procedure resulting in a version 1.0 data image. This stackincludes 06AC33 and 06AH45, but leaves out images with poor seeing, including all of 06AH96(‘-2 hours), giving a total of 16.6 hours of integrationtime. Details on the data reduction procedure will be published in Lin etal. (in preparation). We reach a 5cr depth of 23.8, 0.3 magnitudes deeperthan the original Terapix stack.7Chapter 2. Data2.3 Making the Source CatalogueAll photometry is carried out using SEXTRACTOR (Bertin & Arnouts, 1996).To calibrate the WIRCam photometry, we compare against other availableK data. For an absolute zero-point calibration we use the 2MASS catalogue. As discussed for the astrometry, there are only about 100 unsaturatedsources in both GOODS-N and the 2MASS catalogue. However, this is lessof a problem for calibrating the photometry since we can average over allthe available sources, whereas for calibrating the astrometry all the sourcesare used to define the transformation. The 2MASS catalogue is in Vegamagnitudes, so in order to set an absolute zero-point in the AB system, weuse the WIRCam K-band filter transmission curve to calculate the conversion K(AB) = K(Vega) + 1.844. Comparing mag..auto measured in theWIRCam image with the 2MASS magnitude, we find an absolute zero-pointof 31.915 for the stack.To calibrate the photometry for faint sources, we use the FLAMINGOSJHKS data(Elston et al., 2003). We check for colour gradients in thecomparison between the WIRCam and FLAMINGOS data. Any differencesare within the icr level, so we are confident in the quality of the WIRCamphotometry.We take the approach of detecting all sources and then making a cutto select only the significant sources from the catalogue. The full catalogueincludes all sources with at least three pixels icr above the local backgroundto avoid including cosmic rays. We can then set a threshold cutoff at thedesired signal-to-noise ratio (SNR):m = —2.51og(S) + ZP (2.1)Urn= 2.5 X 0.434()(1.08522SNR(.)The zero-point (ZP) is specific to each image. For the BzK sample selection,we require a 5cr detection in K, meaning Urn 0.217. The noise is measuredfrom the local background and uses the weight map we include for eachimage.S EXTRACTOR outputs internal and external flags. The internal flagsindicate photometric quality. In particular, a flag value of 1 indicates nearbyneighbours, 2 indicates a blended source, 4 indicates saturated pixels andhigher flag values indicate truncation or corruption. We require flags < 48Chapter 2. Dataand rely on small (2” diameter) apertures to measure the flux from eachsource.The external flags report objects in areas of the image with problems.These areas are indicated in a flag map which was produced by Lihwai Linusing WEIGHTWATCHER during data reduction. In the flag map, a valueof 1 indicates areas with low exposure time, 2 indicates pixels affected bycrosstalk and 4 indicates pixels which are saturated and were removed duringdata reduction, usually at the centres of bright stars. The SEXTRACTORparameter tracing the highest value of flagged pixel within the object’s areais ‘imaf lags_iso’, which we require to be zero.2.4 Source countsAs a check on our photometry, we can compare our 5u K-band cataloguesource counts to other K-band data. Figure 2.2 shows our data comparedto the FLAMINGOS data on GOODS-N (the best available prior to ourobservations) and the ISAAC data on GOODS-S. Our number counts arelow at the very bright end, since bright objects near saturation were flaggedduring data reduction and therefore removed from the catalogue. We findthat we are in very good agreement with the other two catalogues up toK 22.5, where our counts are slightly high compared to the GOODS5 number counts. This could be due to cosmicvariance. Since both theGOODS fields are quite small(‘--160 arcseconds2),there may be an overdensity in the northern field compared to the southern field. Our WIRCamdata reach a depth of K(AB) = 23.8, comparable to the ISAAC datainGOODS-S.2.5 GOODS-N multi-wavelength coverageAs discussed in section 1.4, we work in GOODS-N because it has excellentmulti-wavelength coverage. Infrared observations by Spitzer include thefour IRAC bands at 3.6, 4.5, 5.8 and 8.0jm, as well as the MIPS 24tmimaging (Dickinson et al. in preparation). 2 Ms of integration time withChandra provide a very deep X-ray map (Alexander et al., 2003). There arevery deep 1.4 GHz radio data from the VLA (Morrison et al. in preparation).Also, there are submillimetre and millimetre data from JCMT with SCUBAat 850 urn (Pope et al., 2006), AzTEC at 1.1mm (Perera et al., 2008)andfrom TRAM with MAMBO at 1.2 mm (Greve et al., 2008).9Chapter 2. DataVery deep optical data exist from ACS on HST with high angular resolution (Giavalisco et al., 2004). There are also excellent BVRIz’ from theSubaru telescope (Capak et al., 2004). In order to combine the HST datawith our ground-based NIR observations, the ACS data must be degradedto the WIRCam seeing (0.7” from 0.05”). The degraded ACS data wouldthen be comparable in depth to the Subaru data. Additionally, both theWIRCam and Subaru data cover an extended region(20’ x 20’) centredon GOODS-N, while the ACS data cover only GOODS-N-proper. Using theSubaru optical data allows us to select a larger sample to be stacked in theradio map which is also larger than GOODS-N-proper. For these reasonswe chose to use the ground-based Subaru data.2.5.1 RedshiftsThere are many publicly available spectroscopic redshifts in GOODS-N (Cohen et al., 2000; Cowie et al., 2004; Wirth et al., 2004; Chapman et al., 2005;Reddy et al., 2006). These 1500 redshifts cover GOODS-N-proper. Toutilize our full sample area, we use photometric redshifts where spectroscopic redshifts are not available. Mark Brodwin and Lihwai Lin produceda preliminary catalogue of photometric redshifts using the optical and NIRUBVRizJK data from Subaru and WIRCam. The comparison betweenspectroscopic and photometric redshifts are shown in figure 2.3. The photometric redshifts are very good up to z ‘- 1, but unfortunately become lessreliable at z “. 2 where galaxy spectra are not as well-understood.10Chapter 2. Data100i80HKs405-.2002 2.5wavelength (microns)Figure 2.1: Transmission curves for WIRCam’s J, H andK broad-bandfilters (Puget et al., 2004).11Chapter 2. DataII111111 liiiI IIliiiII11111111110GOODS—N WIRCAMGOODS—N FLAMINGOSO.1o I:t I0.01I I I I I I i i I I17 18 19 20 21 22 23 24 25K(AB)Figure 2.2: Number counts for our WIRCam K-band catalogue comparedto the FLAMINGOS data on GOODS-N and the ISAAC data on GOODSS. Our data are almost 1 magnitude deeper than the FLAMINGOS and arecomparable to the ISAAC data.12Chapter 2. Data6r12C)—4ci)S0-‘-00Figure 2.3: Comparison between all available spectroscopic redshifts withour preliminary photometric redshift catalogue. Photometric redshiftswere estimated using optical and NIR UBVRizJK data from Subaru andWIRCam in the extended GOODS-N.0 2 4 6Spectroscopic Redshift13Chapter 3BzK galaxy selectionThe BzK criterion was first proposed in Daddi et al. (2004) as a way toselect z ‘-- 2 galaxies and differentiate between actively star forming andpassively evolving galaxies. For a z = 1.9 galaxy, spectral breaks lie to theblue side of the B filter and in between the z and K filters (see figure 2 inDaddi et al. (2004)). The magnitude difference between bands give the fluxratio (see equation 1.4) which we refer to as a colour. Therefore, the locationof the two spectral breaks will affect the colours as the galaxy spectrum isredshifted.The BzK plane is (z—K) versus (B—z), with BzK (z—K)--(B—z).Actively star forming BzK galaxies (SBzKs) have BzK —0.2. Thiscut in BzK space is roughly parallel to dust reddening effects in galaxytemplates allowing for a robust selection independant of dust content (seefigure 8 in Daddi et al. (2004)). Passively evolving BzK galaxies (PBzKs)have BzK < —0.2fl(z — K) > 2.5. The BzK redshift range is typically1.4 < z < 2.5, with many of the outliers being hard X-ray sources which areassumed to trace AGN contamination. The BzK selection was developedfor galaxies with K 22, but here we can push to fainter limits. Thischapter describes the photometry, matching to the multi-wavelength dataand the redshift distribution we get for our K < 23.8 sample.3.1 PhotometryWe use the Subaru telescope B and z data with our WIRCam K imaging inorder to measure the (B — z) and (z — K) colours of objects in the extendedGOODS-N field. We first align the B and z images to the frame of theK image by matching bright sources to define a transformation and thenreprojecting the B and z images onto the K frame. Using SEXTRACTOR(Bertin & Arnouts, 1996) in dual-image mode, we detect in K and thenmeasure the flux within the same circular apertures in each image. We usesmall, 2” diameter circular apertures to maximize signal-to-noise for thedistant z 2 galaxies. However, we must apply some aperture correctionto approximate total magnitude. Aperture corrections are derived for each14Chapter 3. BzK galaxy selectionband from the stellar sequence, for which the PSF is consistent. We usea larger 4” diameter circular aperture to approximate the total magnitude.The B, z and K aperture corrections are shown in figure 3.1.This procedure gives a sample which is magnitude-limited in K. If thereis no significant detection in B or z, we measure the background flux andset a 5o upper limit on the brightness of the source in that band. In thisway, we can still place the source on the BzK diagram usingm> —2.5log(5 x dS) + ZPbafld, (3.1)with ZPB = 31.046 and ZP = 33.826 taken from Capak et al. (2004). Usingthese 2” diameter circular apertures, the Subaru data reach 5u depths ofB = 27.5 and z = 26.1. Sources with 5u detections in K but not in Band/or z are the cyan arrows in Figure 3.2. Objects detected in B but notin z can be classified as SBzKs, and objects detected in z but not in B canbe classified as PBzKs, while objects with no detection in either B or z areunclassified (UBzKs).To account for slight differences between the filters used to set the BzKselection criterion and those on WIRCam and Subaru, we adjust the (B — z)and (z — K) colours by fitting to the stellar sequence in the BzK diagram assuggested in Daddi et al. (2004). This gives the (B—z),(z—K) plane shownin figure 3.2. There are 5588 BzKs with 5u detections in B, z and K.This number splits into 5563 SBzKs and 25 PBzKs. There are 989 BzKswith > 5u detections in K but not in one or both of B and z: 200 SBzKs,55 PBzKs and 734 unclassified BzKs. The total number of BzK-selectedgalaxies is 6577.3.2 Matching to multi-wavelength dataWe match to all available data described in section 2.4. We search formatches within 1.0” except for IRAC and MIPS catalogues, for which wechoose a smaller matching radius of 0.5” to avoid blending issues. TheWIRCam data cover 900 arcmin2,but we restrict the BzK selection to thecentral 700 arcmin2,which has more significant exposure time. Spectroscopic redshifts are only available in GOODS-N-proper (ie. the 10’ x 16’area). The IRAC and MIPS area is 1/3 the BzK area and most BzKshave IRAC detections in the overlap region. Table 3.1 shows the breakdownof matching criteria and number of matches.15Chapter 3. BzK galaxy selectionwaveband match radius(f’)area (arcmin2) number of matchesX-ray 1.0 400 163IRAC 0.5 220 2313MIPS 0.5 220 645Radio 1.0 900 213spec-zs 1.0 160 346Table 3.1: Multi-wavelength catalogues available for GOODS-N, which havebeen matched with the new BzK catalogue.3.3 Redshift distributionThe BzK criterion was designed to select 1.4 z 2.5 galaxies, and wasoriginally developed for K(AB)< 22 (Daddi et al., 2004). Higher redshiftred galaxies will also be selected, but after removing AGN most low redshiftgalaxies should be excluded. In their initial study, Daddi et al. (2004)found12% contamination from lowredshift ‘interlopers’. There has been evidence in other BzK studies which suggest that the redshift range widensfordeeper surveys (e.g. Reddy et al., 2005). In particular, Dunne et al. (2008)find 30% of BzKs have z < 1.4 for their K 24.9 BzK sample withphotometric redshifts.In order to test the BzK selection redshift distribution,we first match tothe entire sample of available spectroscopic redshifts. The resultingdistribution is shown in the top left panel of figure 3.3. In general, the BzKselectiondoes very well, as the distribution obviouslypeaks within the 1.4 < z 2.5range. We find 17% of galaxies with BzK colours are at z < 1.4, butthisfraction improves to 13% when we remove hard X-ray sources. Not surprisngly, the contamination increases with the depth of the sample as seen inthe top right hand panel of figure 3.3. The hard X-ray detections donotidentify any low redshift AGN for K> 22.We considered the possibility that the extent of the contamination weobserve in the redshift distribution is partially a selection effect. It is easierto obtain spectroscopic redshifts for low redshift galaxies, so perhaps z < 1.4galaxies are over-represented in the GOODS-N spectroscopic catalogues. Inorder to investigate such selection effects, wealso compare to the TKRScatalogue (Team Keck Treasury Redshift; Wirth et al., 2004), which isfluxlimited in R. Only 58 BzKs have TKRS redshifts and 30% of those areat z < 1.4. This R-limited catalogue is almostentirely below z = 1.5 andtherefore is of little help in assessing selection biases in our K-limited sample.We then match to our preliminary photometric redshift catalogue. This16Chapter 3. BzK galaxy selectiondistribution is shown in the lower left panel of figure 3.3. As with the spectroscopic sample, notice the sharp drop in BzKs at the z = 1.4 boundary. Inthis case, the drop in the distribution is more gradual, due to uncertainty inthe photometric redshifts. The low redshift contamination is still only 14%.There are many high redshift galaxies included, but here the photometricredshifts are very uncertain. Notice in the bottom right panel of figure 3.3,that removing hard X-ray sources does not help much at these depths, dueto the X-ray detection limit. Another method of identifying AGN is withthe Spitzer colour diagrams (e.g. Sajina et al., 2005) but extremely deepinfrared data are required. The photometric redshift sample shows a largepeak at z = 1.5. This is likely an artifact of the photometric redshift routine.For both the spectroscopic arid photometric redshift samples, the averageredshift is z = 1.9. This average value is used in section 4.2.3 in the radiostacking analysis.1710.50.5.B(MAG_AUTO)z(MAG_AUTO)20K(MAG_AUTO)Figure 3.1: Derivation of aperture corrections for each of B, z and K.The green points are classified by SEXTRACTOR as stars (ie. setting the‘star_class’ parameter to 1) in K. Averaging m(2”) — m(4”) for the unsaturated stars gives the aperture corrections, which are shown by the dashedred lines.Chapter 3. BzK galaxy selection.1.—.— —;•_____•.15 20 250.5•1•I!:..... •..;f4L4 • -.15 20N.I.- I.25—• 1•.—4.4-i.’15. I:. .i’•4.r2518Chapter 3. BzK galaxy selection432N10—16Figure 3.2: BzK diagram for GOODS-N using WIRCam K5 and Subaru Band z’ photometry. This is designed to select 1.4 z 2.5 galaxies and todifferentiate between actively star forming and passively evolving sources.Galaxies with (z — K) — (B — z) > —0.2 (upper left corner) are star forming(SBzKs), while those with (z — K) > 2.5 (upper right corner) are passive(PBzKs). Cyan arrows show those galaxies detected in K but not in eitherB or z. Galaxies detected only in K are not shown, but are included in thetotal number of unclassified BzKs (UBzKs). Stars (lower right) are fit tothe stellar sequence of Daddi et al. (2004) to account for slight differencesin the filters used here and those used to design the BzK selection.0 2 4(B— z)AB19Chapter 3. BzK galaxy selection40-, 30a)201000 1 2 3Spectroscopic Redshifta),0S2000Figure 3.3: Redshift distributions for BzKs with spectroscopic redshifts(top panels) and photometric redshifts (bottom panels). Left panels showhistograms, while right panels show the redshift dependence on K. Greenshows sources with hard X-ray detections. Red dashed lines delineate theBzK redshift range of 1.4 z 2.5.242220180 1 2 3Spectroscopic Redshift24222018.• _•o.,• •.I __.• •I ••.L.- ••ir• .•• I•••: 1.1 111,111110 1 2Photometric3Redshift0 1 2 3Photometric Redshift20Chapter 4Star formation rateestimators4.1 Stellar massStellar mass is estimated by measuring the amount of light coming from oldstars whose emission peaks at ‘-‘ 1.6 /Lm in the rest-frame. Often, full modelSEDs are fit to available photometry to obtain an estimate for stellar mass.Age and reddening affect the SED and so even though full fitting techniquesmight appear sophisticated, the stellar mass estimate is still uncertain andthe results of studies with variable data quality can be difficult to directlycompare. So instead, we use two simple methods of estimating stellar massdirectly from the NIR rest-frame flux. First, following the approach of Daddiet al. (2004) the stellar mass is estimated from the K-band brightness together with a (B — z) colour correction. This method can be applied toany BzK sample as it uses only the B-, z- and K-band data. Second, andperhaps more robustly, for galaxies with IRAC detections, we can estimatethe 1.6 m flux directly and calibrate that with stellar mass.4.1.1 K-band techniqueIn Daddi et al. (2004), multi-wavelength photometry is fit with SED templates to estimate stellar mass and find a correlation between the SEDestimated stellar mass and observed K-band magnitude:log(M/10”M®)_0.4(Ktot— K”), (4.1)where 21.34. Though the K-band does not sample the rest-frame1.6[tm stellar bump directly, Daddi et al. (2004) found that applying acolour correction term is sufficient to account for the bulk of the effects ofreddening due to redshift and dust:logM = 0.218((z — K)AB—2.29). (4.2)21Chapter 4. Star formation rate estimators4.1.2 1.6tm stellar bump techniqueThe second, perhaps more intuitive method is to simply estimate the fluxat 1.6 tim, the peak of the stellar bump, and then relate that to stellarmass through a single proportionality coefficient. This is a particularlyeffective and straightforward method when IRAC data and redshifts areavailable. ComparingvL at 1.6tm rest-frame to the K-band only stellarmass estimate described above, we obtain figure 4.1 and the conversion fromvL at 1.6gm (rest-frame) to stellar mass islog M[M0] 0.808log((vLv)i6m[W])+ 2.080. (4.3)4.2 Star formation ratesStar formation rate (SFR) is a measure of the mass of stars forming per unittime, typically in units of solar masses per year. The general prescription formeasuring SFR is to measure the amount of light coming from young starsand then use an initial mass function (IMF) to convert to mass of stars.Here, we use the rest-frame ultra-violet, mid-infrared and radio to estimateSFR for our sample of BzKs. The following SFR estimators only work forgalaxies with z 2, so we restrict our sample to the SBzKs with no hardX-ray detections and with redshifts in the desired range of 1.4 z Ultra-violetThe rest-frame ultra-violet (UV, A 900 — 3000 A) directly samples thepeak emission of young, massive stars. The complication with estimatingSFR from UV emission is that dust absorbs UV photons. Galaxies with highSFRs have high gas densities (Kennicutt, 1998) and so in general (for averagemetallicities), these galaxies have more dust attenuation. The timescale forwhich young, massive stars emit strongly in the UV is 100 Myr (Caizetti,2008b). Hence, the comparison between various wavelength SFR estimatorscan depend on the respective timescales and mass ranges sampled. Additinally, a more severe problem lies in calculating dust attenuation. However,a simple relationship between reddening and dust attenuation holds for starburst galaxies (Calzetti et al., 2000), which should be reliable for our activelystar forming ‘SBzK’ sample.Following Daddi et al. (2004) we use the B-band to sample the restframe UV. The B-band is centred at 4350A, sampling rest-frame wavelengthsranging from 1800—1200 A for the BzK redshifts (1.4 z 2.5), with the22Chapter 4. Star formation rate estimatorswell-calibrated dust attenuation at 1500 A (Madau et al., 1998) falling at ouraverage redshift of 1.9. Daddi et al. (2007), found the following correlationbetween reddening estimated through full SED model fitting with the (B—z)colour:E(B — V) = 0.25(B — z+O.l)AB.(4.4)The attenuation factor for the appropriate rest-frame wavelength is foundusing the Calzetti extinction law (Calzetti et al., 2000), i.e. A(1500A)lOxE(B—V), where the B-band is centred at 1500A for z = 1.9. Finally,we assume that dereddening flattens the galaxies’ spectra such that no K-correction is needed to obtain L(1500 A), which can therefore be convertedto a star formation rate:SFR(M®yr’) L(1500A)/(8.85 x 1034WHz’). (4.5)The result is shown in figure 4.2. The sources with spectroscopic redshiftsfollow the relationship log(SFR[M® yr’]) = 0.99 x 1og(M/Mo) —8.8, whilethe sources with only photometric redshifts follow log(SFR[Myr’]) =1.2 x log(M/M0)— 11. The photometric redshift sample is offset fromand shows much more scatter than the spectroscopic redshift sample whichtightly follows the trend. This is likely due to the fact that the photometricredshifts artificially cluster at z -‘ 1.5 and do not accurately represent thetrue redshift distribution (see figure 3.3 and section 3.3 for discussion). Inthis UV SFR method, since the B-band samples around isooA rest-frame,the skewed redshift distribution shows up as an offset in the SFR vs. stellarmass plot. For comparison with other SFR estimators in section 4.3, we usethe spectroscopic redshift best-fit relationship.4.2.2 Mid-infraredAs mentioned above, galaxies with higher SFRs are also dustier. The dustabsorbs UV photons produced by young, massive stars and fe-emits thatradiation in the mid- and far-JR. The observed 24 im flux density samplesthe rest-frame ‘-‘-i 8 im emission. In the local universe, the relationshipbetween 8 im flux and SFR is known to depend on metallicity (Calzettiet al., 2007), but nevertheless we can estimate SFRs assuming an averagemetallicity.In order to go from observed 24 im flux density to SFR, we first estimatethe total JR luminosity by fitting Chary and Elbaz templates (Chary &Elbaz, 2001). We then use the standard relationship from Kennicutt (1998):23Chapter 4. Star formation rate estimatorsSFR(M0yr) = (1.8 x10_b)xLIR(LG). (4.6)The results are shown in figure 4.3. The sources with spectroscopic redshiftsfollow the relationship log(SFR[M yr’]) = 1.5 x log(M/M0)— 13, whilethe sources with only photometric redshifts follow log(SFR[Myr1])=1.5 x log(M/M0)— 14. These lines are in excellent agreement, showing thismethod is less sensitive to redshift distribution than the UV method. Weuse the spectroscopic redshift best-fit relation in section RadioAlthough the radio emission does not directly trace star formation, there isa well-known correlation with the far-infrared. In order to use this we firstfind the rest-frame 1.4 0Hz luminosity density. The rest-frame ‘- 1.4 GHzemission is synchrotron-dominated and so follows a power-law S owith a spectral index a c —0.8 (Condon, 1992). We use the conversionfrom rest-frame 1.4GHz luminosity to total JR luminosity density describedby Condon (1992) and refined by Yun et al. (2001):LJR(LG) = (3.5x10—12)x L1.4GHZ(WHz). (4.7)From the 1.4 GHz VLA radio map, we find 213 individual BzKs with significant radio detections, but only 44 have spectroscopic redshifts and manyof the others have poorly constrained photometric redshifts. From these fewdetections, we obtain figure 4.4. The sources with spectroscopic redshifts follow the relationship log(SFR[M0yr’) = 0.85 x log(M/M0)—6.5, whilethe sources with only photometric redshifts follow log(SFR)Myr’]) =1.7 x log(M/M) — 16. In this case, the best-fit relations do not match wellfor the objects with spectroscopic redshifts and photometric redshifts, butsince there are so few objects, this difference is insignificant.For the many BzKs without radio detections, we can stack at theirlocations in the radio map to find their average flux. We can then use theaverage redshift, z = 1.9, to estimate the SFR from the stacking analysis,including the extended GOODS-N region to maximize the number of objects.We use the K-band method for estimating stellar mass since IRAC data donot exist for this extended region. This BzK sample is sufficiently large thatwe can split it up into five bins of stellar mass. In order to obtain detectionsin as many bins as possible, we create bins with approximately equal totalmass, with the lower mass bins having many more objects than the highermass bins. Stacking analysis was carried out by Glenn Morrison.24Chapter 4. Star formation rate estimatorsFigure 4.5 shows the stacking results for those objects with no radiodetections compared to the binned average for objects with detections andthen the combined average. The binned averages for the radio detectionsfollow the relationship log(SFR{M0yr’]) 0.59 x log(M/M0)— 3.2. Thestacked non-detections follow log (SFR[M® yr1]) = 0.84 x log(M/M®) —7.2.And the combined average follows log(SFR{M0yr’j) 1.4x1og(M/M)—12. We use the best-fit relation for the combined average in section Comparing various SFR estimatorsThe three methods to estimate star formation rate described above arecompared in figure 4.6. We show the UV and mid-IR SFR relations forgalaxies with spectroscopic redshifts and the combined average (binned detections+stacked non-detections) for the radio. We then compare ourmulti-wavelength results with the relation found in Daddi et al. (2007) fora comparable BzK sample in GOODS-S. They find the SFR stellar massrelation following log(SFR[M® yr’]) 0.9 x log(M/M®) — 7.6. Our threerelations follow the same general trend as the Daddi et al. (2007) line, butnone match precisely. Since the selection techniques and SFR estimationmethods were consistent with the previous work, we conclude that high redshift SFR estimators have intrinsic uncertainties and cannot be constrainedfurther without detailed spectroscopic follow-up to determine AGN contamination and possible SED evolution.25Chapter 4. Star formation rate estimators101200’U)U)0-I-)U)10101012(vL)I1.6tm rest(W)Figure 4.1: Comparison of vL at 1.6[tm rest-frame to the K-band onlystellar mass estimate for the BzKs with IRAC detections and redshifts.Green points are outside the 1.4 z 2.5 redshift range for which theK-band only method was calibrated. We set our conversion to stellar mass(equation 4.3) using only the blue points; this is shown by the solid line.1010 101126Chapter 4. Star formation rate estimators1 010000-1 00U)1011012Stellar Mass (M0)Figure 4.2: Ultra-violet star formation rate versus stellar mass. Blue pointsare objects with spectroscopic redshifts, green points are objects with onlyphotometric redshifts and the lines are best fit relationships for each groupof objects. The offset between objects with photometric redshifts and thosewith spectroscopic redshifts is likely due to the artifically large spike inphotometric redshift distribution at z = 1.5.1010 101127Chapter 4. Star formation rate estimators111111I I111111• ..... ..1000 :.... .••.•.••.•... $•••••... •‘..10F F1I 11111 I I I I liii I I I 111110’°1011 1012Stellar Mass (M0)Figure 4.3: Mid-infrared star formation rate versus stellar mass. Blue pointsare objects with spectroscopic redshifts, green points are objects with onlyphotometric redshifts and the lines are best-fit relationships for each groupof objects. The two sets of objects agree very well.28Chapter 4. Star formation rate estimators1 O1000>.0100U)0101Stellar Mass(Me)Figure 4.4: Radio star formation rate versus stellar mass for objects withindividual radio detections. Blue points are objects with spectroscopic redshifts, green points are objects with only photometric redshifts and the linesare best fit relationships for each group of objects. There are too few sourcesto rely on the best fit relationships here; stacking analysis allows us to makemore progress, as described in Section lOll 101229Chapter 4. Star formation rate estimators1 010000a100tI)01011012Stellar Mass (M0)Figure 4.5: Binned radio star formation rate versus stellar mass. The binneddetections are shown in green, stacked non-detections are in blue and thecombined average is in red.1010 101130Chapter 4. Star formation rate estimators1 01000to1011012Figure 4.6: Comparison of SFR estimators described above: UV in blue,Mid-JR in green and Radio in red. The black line is the relationship foundin Daddi et al. (2007) for BzKs in GOODS-S. All lines follow a commontrend and scatter is likely due to the uncertain nature of high redshift SFRestimators.1010 1011Stellar Mass (M®)31Chapter 5Comparing various z 2galaxy populationsWe can compare our sample selection to other z 2 populations to testhow robust the BzK criterion works. Two relevant galaxy populations arethe high star forming submillimetre galaxies and the very red dust obscuredgalaxies. Considering these extreme galaxies within the BzK frameworkhelps us evaluate the robustness of the selection criterion.5.1 Submillimetre galaxiesSubmillimetre galaxies (SMGs) are a population of galaxies which are ultraluminous in the infrared (e.g. Chapman et al., 2005). Pope et al. (2006) usedGOODS-N SCUBA imaging at 850 tm to obtain a sample of 36 objects witha redshift distribution peaking at z = 2 (Pope et al,, 2006). They foundSMGs to haveLIR 6.0x 10’2L0,translating to SFR 1100M0yr’ byequation 4.6.Using the SMG sample, we search our K-band catalogue for sourceswithin 1” of an SMG. Of the 36 SMGs in GOODS-N, 32 are detected in Kwith> 3u (GNO3, GNO9, GN1O and GN21 are not detected). These galaxiesare plotted on the BzK diagram in figure 5.1. The specific classificationand redshift information are given in. table 5.1. Of the 32 SMGs with Kdetections, 17 would be classified as BzKs (though 4 would be removed,as they are detected in the hard X-ray). Of the 15 not classified as BzKs,14 are outside the 1.4 z 2.5 BzK redshift range (only GN12 is insidethe redshift range but not classified as a BzK). The BzK selection has noproblem including these objects with extremely high SFR if they are in theappropriate redshift range.32Chapter 5. Comparing various z 2 galaxy populations5.2 Dust Obscured GalaxiesAnother galaxy population which is luminous in the infrared is the dustobscured galaxies (DOGs, Dye et al., 2008). These galaxies are detected withMIPS 24 tm imaging and are selected for their extreme rest-frame mid-JRto UV flux ratios. The DOG selection criterion requiresS24m/SR>1000,selected to be more extreme than any type of galaxy in the local Universe.As discussed in chapter 4, dust absorbs UV photons from star formation orAGN and emits in the infrared. Thus, a high mid-JR to UV flux is indicativeof an extrememly dusty galaxy. DOGs have a redshift distribution centredat z = 2 (Dye et al., 2008; Fiore et al., 2008; Pope et al., 2008). TheBzK criterion is designed to select galaxies independant of dust content.Therefore, examining the extremely dusty DOG population in the BzKplane is an important test of this aspect of the selection technique.Using the available MIPS 24 ,um data and the Subaru R-band imaging,we select DOGs in figure 5.2. We find 108 galaxies satisfying the DOGcriterion and 102 of these have significant K-band detections. These DOGsare plotted on the BzK diagram in figure 5.3 and nearly all the DOGs lie inthe BzK region. They are not all formally identified as BzKs, since 16 havehard X-ray detections and many are not detected in B, making definitiveclassification challenging. However, in general, the BzK criterion is verysuccessful at identifying these extremely dusty high redshift galaxies.33Chapter 5. Comparing various z ‘- 2 galaxy populationsI I III I4-.41.. /-7a //d2/“18/i -7b0/2%14/ 0N/ 31/131- //200/2°/0/3000- ............... -—1I I I I I Io 2 4 6(B— z)ABFigure 5.1: The Pope et al. (2006) SMGs on the BzK diagram. Openpoints are outside the 1.4 z 2.5 BzK redshift range and hard X-raydetections are circled in green. SMG IDs match those in table 5.1.34Chapter 5. Comparing various z 2 galaxy populationsSMG ID BzK type redshift Hard X-ray detectionGNO1 SBzK 2.415 YGNO2 (1.29) NGNO4a SBzK 2.578 YGNO4b UBzK (2.27) YGNO5 SBzK (2.41) NGNO6 SBzK 1.865 NGNO7a UBzK 1.992 NGNO7b SBzK 1.988 NGNu UBzK (4.14) NGN12 2.003 NGN13 0.475 NGN14 (1.10) YGN15 SBzK 2.743 NGN16 UBzK (1.89) NGN17 SBzK 0.884 NGN18 (2.89) NGN19a SBzK 2.490 NGN19b UBzK (1.85) NGN2O (0.44) NGN22 SBzK 2.505 YGN23 UBzK (3.90) NGN24 SBzK (3.07) NGN25 1.013 YGN26 1.219 NGN28 1.019 NGN3O 1.355 NGN31 0.935 N0N32 SBzK (2.05) NGN34 1.363 N0N37 3.189 NGNO4.2 0.851 NGN2O.2___________(4.03) NTable 5.1: BzK description of GOODS-N SMGs (Pope et al., 2006). Redshifts in parentheses are photometric estimates.35Chapter 5. Comparing various z 2 galaxy populations1 01000U)U)100100Figure 5.2: The DOG selection requiresS24m/SR >1000 (above thedashed red line). The whole GOODS-N sample is shown in blue, with BzKsin green.R-K36Chapter 5. Comparing various z c’-’ 2 galaxy populations434N10Figure 5.3: Dust obscured galaxies on the BzK diagram. Open circles areDOGs outside the 1.4 z 2.5 BzK redshift range. Hard X-ray sourcesare circled in green.0 2 4(B—z)AB37Chapter 6ConclusionPrior to this thesis project, GOODS-N lacked deep NIR data. That gapin the multi-wavelength coverage of GOODS-N has now been filled withJ- and K-band imaging from WIRCam on CFHT. We have improved thedata reduction process to remove severe crosstalk artifacts, ensure accurateastrometry and produce science-grade photometry. We have produced asource catalogue which has already been used for published results (Popeet al., 2008).With our source catalogue, we are able to select a deep sample of z ‘- 2galaxies with the BzK selection technique. Our sample rivals those of thedeepest BzK studies (K(AB) < 23.8). We use the extensive catalogue ofpublicly available spectroscopic redshifts to investigate the redshift distribution for this deep BzK sample. We find that the deep sample follows thedesired BzK redshift range of 1.4 < z < 2.5 very well, with only 13% contamination from low redshift interlopers. We also present a new photometricredshift catalogue using our NIR data. Using these photometric redshiftsto further investigate the redshift distribution of BzKs we find only 14%low redshift contamination. Note that although many high redshift galaxiesare included in our BzK photometric redshift sample, it is unclear whetherthese galaxies are truly at such high redshifts or if our photometric redshiftcatalogue breaks down at these redshifts.We adopt a straightforward approach to estimating stellar mass by simply measuring the luminosity at 1.6 im (rest-frame), at the peak of oldstellar emission. We then estimate star formation rates in three differentways, with the rest-frame UV to sample young stellar emission, the mid-JRto sample the emission from dust heated by star formation and the radiocoming from cosmic ray electrons, which correlates to the far-JR dust emission. Comparing these multi-wavelength star formation rate tracers, we findthat though these techniques follow similar trends, their detailed normalizations are difficult to constrain. In particular, the UV method is sensitive tothe redshift distribution. The mid-JR method is known from local studies tohave severe metallicity dependance, but works relatively well for our sample.With the radio, even using a very deep radio map, stacking analysis is re38Chapter 6. Conclusionquired. In general, our results agree with previous work, though the scatteramong the three methods shows the imprecise nature of high redshift starformation measurements. 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