Photometric redshifts: estimating their contamination and distribution using clustering information

We present a new technique to estimate the level of contamination between photometric redshift bins. If the true angular cross-correlation between redshift bins can be safely assumed to be zero, any measured cross-correlation is a result of contamination between the bins. We present the theory for an arbitrary number of redshift bins, and discuss in detail the case of two and three bins which can be easily solved analytically. We use mock catalogues constructed from the Millennium Simulation to test the method, showing that artificial contamination can be successfully recovered with our method. We find that degeneracies in the parameter space prohibit us from determining a unique solution for the contamination, though constraints are made which can be improved with larger data sets. We then apply the method to an observational galaxy survey: the deep component of the Canada-France-Hawaii Telescope Legacy Survey. We estimate the level of contamination between photometric redshift bins and demonstrate our ability to reconstruct both the true redshift distribution and the true average redshift of galaxies in each photometric bin.Comment: 14 pages, 12 figures, accepted for publication in MNRAS V2: Section 4.4 added. Significant additions to analysis in section 5.

Constraining the photometric properties of MgII absorbing galaxies with the SDSS

Using a sample of nearly 700 quasars with strong (W_0(2796)>0.8 Angstrom) MgII absorption lines detected in the Early Data Release of the SDSS, we demonstrate the feasibility of measuring the photometric properties of the absorber systems by stacking SDSS imaging data. As MgII lines can be observed in the range 0.37<z_abs<2.2, the absorbing galaxies are in general not identified in SDSS images, but they produce systematic light excesses around QSOs which can be detected with a statistical analysis. In this Letter we present a 6-sigma detection of this effect over the whole sample in i-band, rising to 9.4-sigma for a low-redshift subsample with 0.37<z_abs<=0.82. We use a control sample of QSOs without strong MgII absorption lines to quantify and remove systematics with typical 10-20% accuracy. The signal varies as expected as a function of absorber redshift. For the low z_abs subsample we can reliably estimate the average luminosities per MgII absorber system in the g, r, and i bands and find them to be compatible with a few-hundred-Myr old stellar population of M_r ~ -21 in the rest frame. Colors are also consistent with typical absorbing galaxies resembling local Sb-c spirals. Our technique does not require any spectroscopic follow-up and does not suffer from confusion with other galaxies arising along the line-of-sight. It will be applied to larger samples and other line species in upcoming studies.Comment: Accepted on ApJ Letters, 5 pages, 2 figure

Clustering-based Redshift Estimation: Comparison to Spectroscopic Redshifts

We investigate the potential and accuracy of clustering-based redshift estimation using the method proposed by M\'enard et al. (2013). This technique enables the inference of redshift distributions from measurements of the spatial clustering of arbitrary sources, using a set of reference objects for which redshifts are known. We apply it to a sample of spectroscopic galaxies from the Sloan Digital Sky Survey and show that, after carefully controlling the sampling efficiency over the sky, we can estimate redshift distributions with high accuracy. Probing the full colour space of the SDSS galaxies, we show that we can recover the corresponding mean redshifts with an accuracy ranging from $\delta$z=0.001 to 0.01. We indicate that this mapping can be used to infer the redshift probability distribution of a single galaxy. We show how the lack of information on the galaxy bias limits the accuracy of the inference and show comparisons between clustering redshifts and photometric redshifts for this dataset. This analysis demonstrates, using real data, that clustering-based redshift inference provides a powerful data-driven technique to explore the redshift distribution of arbitrary datasets, without any prior knowledge on the spectral energy distribution of the sources.Comment: 13 pages. Submitted to MNRAS. Comments welcom