High redshift galaxies in the ALHAMBRA survey (original) (raw)
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Context. Most observational results on the high redshift restframe UV-bright galaxies are based on samples pinpointed using the so-called dropout technique or Ly-alpha selection. However, the availability of multifilter data now allows the dropout selections to be replaced by direct methods based on photometric redshifts. In this paper we present the methodology to select and study the population of high redshift galaxies in the ALHAMBRA survey data. Aims: Our aim is to develop a less biased methodology than the traditional dropout technique to study the high redshift galaxies in ALHAMBRA and other multifilter data. Thanks to the wide area ALHAMBRA covers, we especially aim at contributing to the study of the brightest, least frequent, high redshift galaxies. Methods: The methodology is based on redshift probability distribution functions (zPDFs). It is shown how a clean galaxy sample can be obtained by selecting the galaxies with high integrated probability of being within a given redshift interval. However, reaching both a complete and clean sample with this method is challenging. Hence, a method to derive statistical properties by summing the zPDFs of all the galaxies in the redshift bin of interest is introduced. Results: Using this methodology we derive the galaxy rest frame UV number counts in five redshift bins centred at z = 2.5,3.0,3.5,4.0, and 4.5, being complete up to the limiting magnitude at mUV(AB) = 24, where mUV refers to the first ALHAMBRA filter redwards of the Ly-alpha line. With the wide field ALHAMBRA data we especially contribute to the study of the brightest ends of these counts, accurately sampling the surface densities down to mUV(AB) = 21-22. Conclusions: We show that using the zPDFs it is easy to select a very clean sample of high redshift galaxies. We also show that it is better to do statistical analysis of the properties of galaxies using a probabilistic approach, which takes into account both the incompleteness and contamination issues in a natural way.
Dark-Halo Masses, Star Formation Rates, and Stellar Masses of High-Redshift Galaxies
2008
We present a clustering analysis for Lyman-break galaxies (LBGs) at z ˜ 3 -- 5 and K-band selected BzK galaxies at z ˜ 2 using wide-field multi-color data on two blank fields: the Subaru Deep Field and the Subaru/XMM-Newton Deep Field. We then combine our clustering measurements with those taken from the literature, to discuss the dependence of the star-formation rate and stellar mass of galaxies on the mass of hosting dark haloes for high-z galaxies. We find that the star-formation rate of LBGs linearly correlates with the mass of hosting dark haloes, and that within rather large uncertainties, dusty star-burst galaxies (distant red galaxies, ultra-luminous infrared galaxies, and submillimeter galaxies) appear to be simple scaled-up populations of LBGs in terms of their star-formation efficiency. We also find a positive correlation between the stellar mass and the dark-halo mass for K-selected galaxies. A detailed analysis for z ˜ 3 LBGs reveals that there are upper limits on the s...
Estimating the Redshift Distribution of Faint Galaxy Samples
Mon Notic Roy Astron Soc, 2008
We present an empirical method for estimating the underlying redshift distribution N(z) of galaxy photometric samples from photometric observables. The method does not rely on photometric redshift (photo-z) estimates for individual galaxies, which typically suffer from biases. Instead, it assigns weights to galaxies in a spectroscopic subsample such that the weighted distributions of photometric observables (e.g., multi-band magnitudes) match the corresponding distributions for the photometric sample. The weights are estimated using a nearest-neighbor technique that ensures stability in sparsely populated regions of color-magnitude space. The derived weights are then summed in redshift bins to create the redshift distribution. We apply this weighting technique to data from the Sloan Digital Sky Survey as well as to mock catalogs for the Dark Energy Survey, and compare the results to those from the estimation of photo-z's derived by a neural network algorithm. We find that the weighting method accurately recovers the underlying redshift distribution, typically better than the photo-z reconstruction, provided the spectroscopic subsample spans the range of photometric observables covered by the photometric sample.