Accurate Photometric Redshifts Research Articles

CONSTRAINTS ON PHOTOIONIZATION FEEDBACK FROM NUMBER COUNTS OF ULTRA-FAINT HIGH-REDSHIFT GALAXIES IN THE FRONTIER FIELDS

ABSTRACT We exploit a sample of ultra-faint high-redshift galaxies (demagnified Hubble Space Telescope, HST, H 160 magnitude > 30) in the Frontier Fields clusters A2744 and M0416 to constrain a theoretical model for the UV luminosity function in the presence of photoionization feedback. The objects have been selected on the basis of accurate photometric redshifts computed from multi-band photometry including seven HST bands and deep K s and IRAC observations. Magnification is computed on an object-by-object basis from all available lensing models of the two clusters. We take into account source detection completeness as a function of luminosity and size, magnification effects, and systematics in the lens modeling of the clusters under investigation. We find that our sample of high-z galaxies constrain the cutoff halo circular velocity below which star formation is suppressed by photoionization feedback to v c cut < 50 km s−1. This circular velocity corresponds to a halo mass of ≈5.6 × 109 M ⊙ and ≈2.3 × 109 M ⊙ at z = 5 and 10, respectively: higher-mass halos can thus sustain continuous star formation activity without being quenched by external ionizing flux. More stringent constraints are prevented by the uncertainty in the modeling of the cluster lens, as embodied by systematic differences among the lens models available.

The VIPERS Multi-Lambda Survey

We present observations collected in the CFHTLS-VIPERS region in the ultraviolet (UV) with the GALEX satellite (far and near UV channels) and the near infrared with the CFHT/WIRCam camera ($K_s$-band) over an area of 22 and 27 deg$^2$, respectively. The depth of the photometry was optimized to measure the physical properties (e.g., SFR, stellar masses) of all the galaxies in the VIPERS spectroscopic survey. The large volume explored by VIPERS will enable a unique investigation of the relationship between the galaxy properties and their environment (density field and cosmic web) at high redshift (0.5 < z < 1.2). In this paper, we present the observations, the data reductions and the build-up of the multi-color catalogues. The CFHTLS-T0007 (gri-{\chi}^2) images are used as reference to detect and measure the $K_s$-band photometry, while the T0007 u-selected sources are used as priors to perform the GALEX photometry based on a dedicated software (EMphot). Our final sample reaches $NUV_{AB}$~25 (at 5{\sigma}) and $K_{AB}$~22 (at 3{\sigma}). The large spectroscopic sample (~51,000 spectroscopic redshifts) allows us to highlight the robustness of our star/galaxy separation, and the reliability of our photometric redshifts with a typical accuracy $\sigma_z \le$ 0.04 and a catastrophic failure rate {\eta} < 2% down to i~23. We present various tests on the $K_s$ band completeness and photometric redshift accuracy by comparing with existing, overlapping deep photometric catalogues. Finally, we discuss the BzK sample of passive and active galaxies at high redshift and the evolution of galaxy morphology in the (NUV-r) vs (r-K_s) diagram at low redshift (z < 0.25) thanks to the high image quality of the CFHTLS. The images, catalogues and photometric redshifts for 1.5 million sources (down to $NUV \le$ 25 or $K_s \le$ 22) are released and available at this URL: http://cesam.lam.fr/vipers-mls/

LEVERAGING 3D-HST GRISM REDSHIFTS TO QUANTIFY PHOTOMETRIC REDSHIFT PERFORMANCE

ABSTRACT We present a study of photometric redshift accuracy in the 3D-HST photometric catalogs, using 3D-HST grism redshifts to quantify and dissect trends in redshift accuracy for galaxies brighter than JH IR &gt; 24 with an unprecedented and representative high-redshift galaxy sample. We find an average scatter of 0.0197 ± 0.0003(1 + z) in the Skelton et al. photometric redshifts. Photometric redshift accuracy decreases with magnitude and redshift, but does not vary monotonically with color or stellar mass. The 1σ scatter lies between 0.01 and 0.03 (1 + z) for galaxies of all masses and colors below z &lt; 2.5 (for JH IR &lt; 24), with the exception of a population of very red (U − V &gt; 2), dusty star-forming galaxies for which the scatter increases to ∼0.1 (1 + z). We find that photometric redshifts depend significantly on galaxy size; the largest galaxies at fixed magnitude have photo-zs with up to ∼30% more scatter and ∼5 times the outlier rate. Although the overall photometric redshift accuracy for quiescent galaxies is better than that for star-forming galaxies, scatter depends more strongly on magnitude and redshift than on galaxy type. We verify these trends using the redshift distributions of close pairs and extend the analysis to fainter objects, where photometric redshift errors further increase to ∼0.046 (1 + z) at . We demonstrate that photometric redshift accuracy is strongly filter dependent and quantify the contribution of multiple filter combinations. We evaluate the widths of redshift probability distribution functions and find that error estimates are underestimated by a factor of ∼1.1–1.6, but that uniformly broadening the distribution does not adequately account for fitting outliers. Finally, we suggest possible applications of these data in planning for current and future surveys and simulate photometric redshift performance in the Large Synoptic Survey Telescope, Dark Energy Survey (DES), and combined DES and Vista Hemisphere surveys.

Photometric redshift estimation for quasars by integration of KNN and SVM

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches (such as the k-nearest neighbor (KNN) algorithm). In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine (SVM) methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding zphot. Our analysis has found two dense regions with catastrophic failure, one in the range of zphot ϵ [0.3, 1.2] and the other in the range of zphot ϵ [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey (SDSS) quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different |δz| ranges and root mean square (rms) error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN (71.96%, 83.78%, 89.73% and 0.204).

THE ALHAMBRA SURVEY: EVOLUTION OF GALAXY SPECTRAL SEGREGATION

ABSTRACT We study the clustering of galaxies as a function of spectral type and redshift in the range 0.35 &lt; z &lt; 1.1 using data from the Advanced Large Homogeneous Area Medium Band Redshift Astronomical (ALHAMBRA) survey. The data cover 2.381 deg2 in 7 fields, after applying a detailed angular selection mask, with accurate photometric redshifts [ &sgr; z &lt; 0.014 ( 1 &plus; z ) ] ?> down to I AB &lt; 24. From this catalog we draw five fixed number density redshift-limited bins. We estimate the clustering evolution for two different spectral populations selected using the ALHAMBRA-based photometric templates: quiescent and star-forming galaxies. For each sample we measure the real-space clustering using the projected correlation function. Our calculations are performed over the range [0.03, 10.0] h −1 Mpc, allowing us to find a steeper trend for r p &lsim; 0.2 h &minus; 1 ?> Mpc, which is especially clear for star-forming galaxies. Our analysis also shows a clear early differentiation in the clustering properties of both populations: star-forming galaxies show weaker clustering with evolution in the correlation length over the analyzed redshift range, while quiescent galaxies show stronger clustering already at high redshifts and no appreciable evolution. We also perform the bias calculation where similar segregation is found, but now it is among the quiescent galaxies where a growing evolution with redshift is clearer (abrigatted). These findings clearly corroborate the well-known color–density relation, confirming that quiescent galaxies are mainly located in dark matter halos that are more massive than those typically populated by star-forming galaxies.

Pan-STARRS1 variability of XMM-COSMOS AGN

[Abbreviated] Upcoming large area sky surveys like EUCLID and eROSITA crucially depend on accurate photometric redshifts (photo-z). The identification of variable sources, such as AGNs, and the achievable redshift accuracy for varying objects are important in view of the science goals of the EUCLID and eROSITA missions. We probe AGN optical variability for a large sample of X-ray-selected AGNs in the XMM-COSMOS field, using the light curves provided by the Pan-STARRS1 (PS1) 3pi and MDF04 surveys. Utilizing two different variability parameters, we defined a sample of varying AGNs for every PS1 band. We investigated the influence of variability on the calculation of photo-z by applying three different input photometry sets for our fitting procedure. For each of the five PS1 bands, we chose either the epochs minimizing the interval in observing time, the median magnitude values, or randomly drawn light curve points to compute the redshift. In addition, we derived photo-z using PS1 photometry extended by GALEX/IRAC bands. We find that the photometry produced by the 3pi survey is sufficient to reliably detect variable sources provided that the fractional variability amplitude is at least 3%. Considering the photo-z of variable AGNs, we observe that minimizing the time spacing of the chosen points yields superior photo-z in terms of the percentage of outliers (33%) and accuracy (0.07), outperforming the other two approaches. Drawing random points from the light curve gives rise to typically 57% of outliers and an accuracy of 0.4. Adding GALEX/IRAC bands for the redshift determination weakens the influence of variability. Although the redshift quality generally improves when adding these bands, we still obtain not less than 26% of outliers and an accuracy of 0.05 at best, therefore variable sources should receive a flag stating that their photo-z may be low quality.

A sparse Gaussian process framework for photometric redshift estimation

Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshift estimation is presented. Simulated and real data from SDSS DR12 were used to train and test the proposed models. We show that approaches which include careful data preparation and model design offer a significant improvement in comparison with several competing machine learning algorithms. Standard implementations of most regression algorithms have as the objective the minimization of the sum of squared errors. For redshift inference, however, this induces a bias in the posterior mean of the output distribution, which can be problematic. In this paper we directly target minimizing $\Delta z = (z_\textrm{s} - z_\textrm{p})/(1+z_\textrm{s})$ and address the bias problem via a distribution-based weighting scheme, incorporated as part of the optimization objective. The results are compared with other machine learning algorithms in the field such as Artificial Neural Networks (ANN), Gaussian Processes (GPs) and sparse GPs. The proposed framework reaches a mean absolute $\Delta z = 0.0026(1+z_\textrm{s})$, over the redshift range of $0 \le z_\textrm{s} \le 2$ on the simulated data, and $\Delta z = 0.0178(1+z_\textrm{s})$ over the entire redshift range on the SDSS DR12 survey, outperforming the standard ANNz used in the literature. We also investigate how the relative size of the training set affects the photometric redshift accuracy. We find that a training set of \textgreater 30 per cent of total sample size, provides little additional constraint on the photometric redshifts, and note that our GP formalism strongly outperforms ANNz in the sparse data regime for the simulated data set.

Accurate PSF-matched photometry and photometric redshifts for the extreme deep field with the Chebyshev–Fourier functions

Photometric redshifts, which have become the cornerstone of several of the largest astronomical surveys like PanStarrs, DES, J-PAS or the LSST, require precise measurements of galaxy photometry in different bands using a consistent physical aperture. This is not trivial, due to the variation in the shape and width of the Point Spread Function (PSF) introduced by wavelength differences, instrument positions and atmospheric conditions. Current methods to correct for this effect rely on a detailed knowledge of the PSF characteristics as a function of the survey coordinates, which can be difficult due to the relative paucity of stars tracking the PSF behaviour. Here we show that it is possible to measure accurate, consistent multicolour photometry without knowing the shape of PSF. The Chebyshev-Fourier Functions (CHEFs) can fit the observed profile of each object and produce high signal-to-noise integrated flux measurements unaffected by the PSF. These total fluxes, which encompass all the galaxy populations, are much more useful for Galaxy Evolution studies than aperture photometry. We compare the total magnitudes and colours obtained using our software to traditional photometry with SExtractor, using real data from the COSMOS survey and the Hubble Ultra Deep Field. We also apply the CHEFs technique to the recently published Extreme Deep Field and compare the results to those from ColorPro on the HUDF. We produce a photometric catalogue with 35732 sources (10823 with S/N>5), reaching a photometric redshift precision of 2% due to the extraordinary depth and wavelength coverage of the XDF images.

Galaxy clustering using photometric redshifts

We investigate the evolution of the galaxy two point correlation function (CF) over a wide redshift range, 0.2 < z < 3. For the first time the systematic analysis covers the redshifts above 1 - 1.5. The catalogue of ~250000 galaxies with i+ < 25 and known photometric redshifts in the Subaru Deep field is used. The galaxies are divided into three luminosity classes and several distance/redshift bins. First, the 2D CF is determined for each luminosity class and distance bin. Calculations are based on the quantitative differences between the surface distributions of galaxy pairs with comparable and distinctly different photometric redshifts. The power law approximation for the CF is used. A limited accuracy of photometric redshifts as compared to the spectroscopic ones has been examined and taken into account. Then, the 3D functions for all the selected luminosities and distances are calculated. The power-law parameters of the CF, the slope and the correlation length, are determined. Both parameters do not show strong variations over the whole investigated redshift range. The slope of the luminous galaxies appears to be consistently steeper than that for the fainter ones. The linear bias factor, b(z), grows systematically with redshift; assuming the local normalization b(0) = 1.1-1.2, the bias reaches 3 - 3.5 at the high redshift limit.

Accurate photometric redshift probability density estimation – method comparison and application

We introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and galaxy samples. As a use case we apply our method to CFHTLS galaxies. The ordinal classification algorithm treats distinct redshift bins as ordered values, which improves the quality of photometric redshift PDFs, compared with non-ordinal classification architectures. We also propose a new single value point estimate of the galaxy redshift, that can be used to estimate the full redshift PDF of a galaxy sample. This method is competitive in terms of accuracy with contemporary algorithms, which stack the full redshift PDFs of all galaxies in the sample, but requires orders of magnitudes less storage space. The methods described in this paper greatly improve the log-likelihood of individual object redshift PDFs, when compared with a popular Neural Network code (ANNz). In our use case, this improvement reaches 50\% for high redshift objects ($z \geq 0.75$). We show that using these more accurate photometric redshift PDFs will lead to a reduction in the systematic biases by up to a factor of four, when compared with less accurate PDFs obtained from commonly used methods. The cosmological analyses we examine and find improvement upon are the following: gravitational lensing cluster mass estimates, modelling of angular correlation functions, and modelling of cosmic shear correlation functions.

Evolution of mid-infrared galaxy luminosity functions from the entireAKARINEP deep field with new CFHT photometry

We present infrared galaxy luminosity functions (LFs) in the AKARI North Ecliptic Pole (NEP) deep field using recently-obtained, wider CFHT optical/near-IR images. AKARI has obtained deep images in the mid-infrared (IR), covering 0.6 deg$^2$ of the NEP deep field. However, our previous work was limited to the central area of 0.25 deg$^2$ due to the lack of optical coverage of the full AKARI NEP survey. To rectify the situation, we recently obtained CFHT optical and near-IR images over the entire AKARI NEP deep field. These new CFHT images are used to derive accurate photometric redshifts, allowing us to fully exploit the whole AKARI NEP deep field. AKARI's deep, continuous filter coverage in the mid-IR wavelengths (2.4, 3.2, 4.1, 7, 9, 11, 15, 18, and 24$\mu$m) exists nowhere else, due to filter gaps of other space telescopes. It allows us to estimate restframe 8$\mu$m and 12$\mu$m luminosities without using a large extrapolation based on spectral energy distribution (SED) fitting, which was the largest uncertainty in previous studies. Total infrared luminosity (TIR) is also obtained more reliably due to the superior filter coverage. The resulting restframe 8$\mu$m, 12$\mu$m, and TIR LFs at $0.15<z<2.2$ are consistent with previous works, but with reduced uncertainties, especially at the high luminosity-end, thanks to the wide field coverage. In terms of cosmic infrared luminosity density ($\Omega_{\mathrm{IR}}$), we found that the $\Omega_{\mathrm{IR}}$ evolves as $\propto (1+z)^{4.2\pm 0.4}$.

Analytic photometric redshift estimator for Type Ia supernovae from the Large Synoptic Survey Telescope

Accurate and precise photometric redshifts (photo-z's) of Type Ia supernovae (SNe Ia) can enable the use of SNe Ia, measured only with photometry, to probe cosmology. This dramatically increases the science return of supernova surveys planned for the Large Synoptic Survey Telescope (LSST). In this paper we describe a significantly improved version of the simple analytic photo-z estimator proposed by Wang (2007) and further developed by Wang, Narayan, and Wood-Vasey (2007). We apply it to 55,422 simulated SNe Ia generated using the SNANA package with the LSST filters. We find that the estimated errors on the photo-z's, \sigma_{z_{phot}}/(1+z_{phot}), can be used as filters to produce a set of photo-z's that have high precision, accuracy, and purity. Using SN Ia colors as well as SN Ia peak magnitude in the i band, we obtain a set of photo-z's with 2 percent accuracy (with \sigma(z_{phot}-z_{spec})/(1+z_{spec}) = 0.02), a bias in z_{phot} (the mean of z_{phot}-z_{spec}) of -9 X 10^{-5}, and an outlier fraction (with |(z_{phot}-z_{spec})/(1+z_{spec})|>0.1) of 0.23 percent, with the requirement that \sigma_{z_{phot}}/(1+z_{phot})<0.01. Using the SN Ia colors only, we obtain a set of photo-z's with similar quality by requiring that \sigma_{z_{phot}}/(1+z_{phot})<0.007; this leads to a set of photo-z's with 2 percent accuracy, a bias in z_{phot} of 5.9 X 10^{-4}, and an outlier fraction of 0.32 percent.

THE NEXT GENERATION VIRGO CLUSTER SURVEY. XV. THE PHOTOMETRIC REDSHIFT ESTIMATION FOR BACKGROUND SOURCES

The Next Generation Virgo Cluster Survey is an optical imaging survey covering 104 deg^2 centered on the Virgo cluster. Currently, the complete survey area has been observed in the u*giz-bands and one third in the r-band. We present the photometric redshift estimation for the NGVS background sources. After a dedicated data reduction, we perform accurate photometry, with special attention to precise color measurements through point spread function-homogenization. We then estimate the photometric redshifts with the Le Phare and BPZ codes. We add a new prior which extends to iAB = 12.5 mag. When using the u*griz-bands, our photometric redshifts for 15.5 \le i \lesssim 23 mag or zphot \lesssim 1 galaxies have a bias |\Delta z| < 0.02, less than 5% outliers, and a scatter \sigma_{outl.rej.} and an individual error on zphot that increase with magnitude (from 0.02 to 0.05 and from 0.03 to 0.10, respectively). When using the u*giz-bands over the same magnitude and redshift range, the lack of the r-band increases the uncertainties in the 0.3 \lesssim zphot \lesssim 0.8 range (-0.05 < \Delta z < -0.02, \sigma_{outl.rej} ~ 0.06, 10-15% outliers, and zphot.err. ~ 0.15). We also present a joint analysis of the photometric redshift accuracy as a function of redshift and magnitude. We assess the quality of our photometric redshifts by comparison to spectroscopic samples and by verifying that the angular auto- and cross-correlation function w(\theta) of the entire NGVS photometric redshift sample across redshift bins is in agreement with the expectations.

CANDELS/GOODS-S, CDFS, AND ECDFS: PHOTOMETRIC REDSHIFTS FOR NORMAL AND X-RAY-DETECTED GALAXIES

We present photometric redshifts and associated probability distributions for all detected sources in the Extended Chandra Deep Field South (ECDFS). This work makes use of the most up-to-date data from the Cosmic Assembly Near-IR Deep Legacy Survey (CANDELS) and the Taiwan ECDFS Near-Infrared Survey (TENIS) in addition to other data. We also revisit multi-wavelength counterparts for published X-ray sources from the 4 Ms CDFS and 250 ks ECDFS surveys, finding reliable counterparts for 1207 out of 1259 sources (∼96%). Data used for photometric redshifts include intermediate-band photometry deblended using the TFIT method, which is used for the first time in this work. Photometric redshifts for X-ray source counterparts are based on a new library of active galactic nuclei/galaxy hybrid templates appropriate for the faint X-ray population in the CDFS. Photometric redshift accuracy for normal galaxies is 0.010 and for X-ray sources is 0.014 and outlier fractions are 4% and 5.2%, respectively. The results within the CANDELS coverage area are even better, as demonstrated both by spectroscopic comparison and by galaxy-pair statistics. Intermediate-band photometry, even if shallow, is valuable when combined with deep broadband photometry. For best accuracy, templates must include emission lines.

A NEW METHOD TO SEARCH FOR HIGH-REDSHIFT CLUSTERS USING PHOTOMETRIC REDSHIFTS

We describe a new method (Poisson Probability Method, PPM) to search for high redshift galaxy clusters and groups by using photometric redshift information and galaxy number counts. The method relies on Poisson statistics and is primarily introduced to search for Mpc-scale environments around a specific beacon. The PPM is tailored to both the properties of the FR I radio galaxies in the Chiaberge et al. (2009) sample, that are selected within the COSMOS survey, and on the specific dataset used. We test the efficiency of our method of searching for cluster candidates against simulations. Two different approaches are adopted. i) We use two z~1 X-ray detected cluster candidates found in the COSMOS survey and we shift them to higher redshift up to z=2. We find that the PPM detects the cluster candidates up to z=1.5, and it correctly estimates both the redshift and size of the two clusters. ii) We simulate spherically symmetric clusters of different size and richness, and we locate them at different redshifts (i.e. z = 1.0,1.5, and 2.0) in the COSMOS field. We find that the PPM detects the simulated clusters within the considered redshift range with a statistical 1-sigma redshift accuracy of ~0.05. The PPM is an efficient alternative method for high-redshift cluster searches that may also be applied to both present and future wide field surveys such as SDSS Stripe 82, LSST, and Euclid. Accurate photometric redshifts and a survey depth similar or better than that of COSMOS (e.g. I<25) are required.

Constraints on the merging channel of massive galaxies since z ∼ 1

(Abridged) We probe the merging channel of massive galaxies over the z=0.3-1.3 redshift window by studying close pairs in a sample of 238 galaxies with stellar mass >1E11Msun, from the deep (m<26.5AB, 3 sigma) SHARDS survey. SHARDS provides medium band photometry equivalent to low-resolution optical spectra (R~50), allowing us to obtain extremely accurate photometric redshifts (|Dz|/(1+z)~0.55%) and to improve the constraints on the age distribution of the stellar populations. A strong correlation is found between the age difference of central and satellite galaxy and stellar mass ratio, from negligible age differences in major mergers to age differences ~4 Gyr for 1:100 minor mergers. However, this correlation is simply a reflection of the mass-age trend in the general population. The dominant contributor to the growth of massive galaxies corresponds to mass ratios mu=Msat/Mcen>0.3, followed by a decrease in the fractional mass growth rate linearly proportional to log mu, at least down to mu~0.01, suggesting a decreasing role of mergers involving low-mass satellites, especially if dynamical friction timescales are taken into account. A simple model results in an upper limit for the average mass growth rate of massive galaxies of DM/M/Dt~ 0.08+-0.02 per Gyr, over the z<1 range, with a ~70% fractional contribution from (major) mergers with mu>0.3. The majority of the stellar mass contributed by mergers does not introduce significantly younger populations, in agreement with the small radial age gradients observed in present-day early-type galaxies.

CLASH: A CENSUS OF MAGNIFIED STAR-FORMING GALAXIES ATz∼ 6-8

We utilize 16 band Hubble Space Telescope (HST) observations of 18 lensing clusters obtained as part of the Cluster Lensing And Supernova survey with Hubble (CLASH) Multi-Cycle Treasury program to search for $z\sim6-8$ galaxies. We report the discovery of 204, 45, and 13 Lyman-break galaxy candidates at $z\sim6$, $z\sim7$, and $z\sim8$, respectively, identified from purely photometric redshift selections. This large sample, representing nearly an order of magnitude increase in the number of magnified star-forming galaxies at $z\sim 6-8$ presented to date, is unique in that we have observations in four WFC3/UVIS UV, seven ACS/WFC optical, and all five WFC3/IR broadband filters, which enable very accurate photometric redshift selections. We construct detailed lensing models for 17 of the 18 clusters to estimate object magnifications and to identify two new multiply lensed $z \gtrsim 6$ candidates. The median magnifications over the 17 clusters are 4, 4, and 5 for the $z\sim6$, $z\sim7$, and $z\sim8$ samples, respectively, over an average area of 4.5 arcmin$^2$ per cluster. We compare our observed number counts with expectations based on convolving "blank" field UV luminosity functions through our cluster lens models and find rough agreement down to $\sim27$ mag, where we begin to suffer significant incompleteness. In all three redshift bins, we find a higher number density at brighter observed magnitudes than the field predictions, empirically demonstrating for the first time the enhanced efficiency of lensing clusters over field surveys. Our number counts also are in general agreement with the lensed expectations from the cluster models, especially at $z\sim6$, where we have the best statistics.

The WIRCam Deep Survey

We present an analysis of the clustering of galaxies from z ≈ 2 to the present day using the WIRCam Deep Survey (WIRDS). WIRDS combines deep optical data from the CFHTLS Deep fields with its own deep near-infrared data, providing a photometric data-set over an effective area of 2.4 deg2, from which accurate photometric redshifts and stellar masses can be estimated. We use the data to calculate the angular correlation function for galaxy samples split by star-formation activity, stellar mass and redshift. Using WIRDS with its large total area and multiple fields gives a low cosmic variance contribution to the error, which we estimate to be less than ~2.8%. Based on power-law fits, we estimate the real-space clustering for each sample, determining clustering lengths and power-law slopes. For galaxies selected by constant mass, we find that the clustering scale shows no evolution up to z ≈ 2. Splitting the galaxy sample by mass, we see a consistent trend for higher mass galaxies to have larger clustering scales at all redshifts considered. We use our results to test the galform semi-analytical model of galaxy formation and evolution. The observed trends are well matched by the model galaxies for both the redshift evolution and the mass dependence of the galaxy clustering. We split the galaxy population into passive and star-forming populations based on rest-frame dust-corrected NUV-r colours. We find that the passive galaxy populations show a significantly larger clustering scale at all redshifts than the star-forming population below masses of M⋆ ~ 1011 h-1 M⊙, showing that even at z ≈ 2 passive galaxies exist in denser environments than the bulk of the star-forming galaxy population. For star-forming galaxies with stellar masses of M⋆ ≳ 1011 h-1 M⊙, we find a clustering strength of ~8 h-1 Mpc across all redshifts, comparable to the measurements for the passive population. Additionally, for star-forming galaxies we see that clustering strength increases for higher stellar mass systems, however little sign of a mass dependence in passive galaxies is observed over the range in stellar mass that is probed. Comparing our results to the model galaxy population produced by galform, we find a qualitative good agreement between the model predictions and the observed clustering. Finally, we investigate the connection between galaxy stellar mass and dark matter halo mass, showing a clear correlation between the two in both the WIRDS data and the galform predictions.

A catalogue of photometric redshifts for the SDSS-DR9 galaxies

Accurate photometric redshifts for large samples of galaxies are among the main products of modern multiband digital surveys. Over the last decade, the Sloan Digital Sky Survey (SDSS) has become a sort of benchmark against which to test the various methods. We present an application of a new method to the estimation of photometric redshifts for the galaxies in the SDSS Data Release 9 (SDSS-DR9). Photometric redshifts for more than 143 million galaxies were produced and made available at the URL: http://dame.dsf.unina.it/catalog/DR9PHOTOZ/. The MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) model provided within the framework of the DAMEWARE (DAta Mining and Exploration Web Application REsource) is an interpolative method derived from machine learning models. The obtained redshifts have an overall uncertainty of sigma=0.023 with a very small average bias of about 3x10^-5, and a fraction of catastrophic outliers of about 5%. This result is slightly better than what was already available in the literature, particularly in terms of the smaller fraction of catastrophic outliers.

PROBABILITY FRIENDS-OF-FRIENDS (PFOF) GROUP FINDER: PERFORMANCE STUDY AND OBSERVATIONAL DATA APPLICATIONS ON PHOTOMETRIC SURVEYS

(Abridged) In tandem with observational datasets, we utilize realistic mock catalogs, based on a semi-analytic galaxy formation model, constructed specifically for Pan-STARRS1 Medium Deep Surveys in order to assess the performance of the Probability Friends-of-Friends (PFOF, Liu et al.) group finder, and aim to develop a grouping optimization method applicable to surveys like Pan-STARRS1. Producing mock PFOF group catalogs under a variety of photometric redshift accuracies ({\sigma}{\Delta}z/(1+zs)), we find that catalog purities and completenesses from ``good' {\sigma}{\Delta}z/(1+zs)) ~ 0.01) to ``poor' {\sigma}{\Delta}z/(1+zs)) ~ 0.07) photo-zs gradually degrade respectively from 77% and 70% to 52% and 47%. To avoid model dependency of the mock for use on observational data we apply a ``subset optimization' approach, using spectroscopic-redshift group data from the target field to train the group finder for application to that field, as an alternative method for the grouping optimization. We demonstrate this approach using these spectroscopically identified groups as the training set, i.e. zCOSMOS groups for PFOF searches within PS1 Medium Deep Field04 (PS1MD04) and DEEP2 EGS groups for searches in PS1MD07. We ultimately apply PFOF to four datasets spanning the photo-z uncertainty range from 0.01 to 0.06 in order to quantify the dependence of group recovery performance on photo-z accuracy. We find purities and completenesses calculated from observational datasets broadly agree with their mock analogues. Further tests of the PFOF algorithm are performed via matches to X-ray clusters identified within the PS1MD04 and COSMOS footprints. Across over a decade in group mass, we find PFOF groups match ~85% of X-ray clusters in COSMOS and PS1MD04, but at a lower statistical significance in the latter.