A new method to improve photometric redshift reconstruction

Context. In the next decade, the Large Synoptic Survey Telescope (LSST) will become a major facility for the astronomical community. However, accurately determining the redshifts of the observed galaxies without using spectroscopy is a major challenge. Aims. Reconstruction of the redshifts with high resolution and well-understood uncertainties is mandatory for many science goals, including the study of baryonic acoustic oscillations (BAO). We investigate different approaches to establish the accuracy that can be reached by the LSST six-band photometry. Methods. We construct a realistic mock galaxy catalog, based on the Great Observatories Origins Deep Survey (GOODS) luminosity function, by simulating the expected apparent magnitude distribution for the LSST. To reconstruct the photometric redshifts (photo-z’s), we consider a template-fitting method and a neural network method. The photo-z reconstruction from both of these techniques is tested on real Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) data and also on simulated catalogs. We describe a new method to improve photometric redshift reconstruction that efficiently removes catastrophic outliers via a likelihood ratio statistical test. This test uses the posterior probability functions of the fit parameters and the colors. Results. We show that the photometric redshift accuracy will meet the stringent LSST requirements up to redshift ~2.5 after a selection that is based on the likelihood ratio test or on the apparent magnitude for galaxies with signal-to-noise ratio S/N > 5 in at least 5 bands. The former selection has the advantage of retaining roughly 35% more galaxies for a similar photo-z performance compared to the latter. Photo-z reconstruction using a neural network algorithm is also described. In addition, we utilize the CFHTLS spectro-photometric catalog to outline the possibility of combining the neural network and template-fitting methods. Conclusions. We demonstrate that the photometric redshifts will be accurately estimated with the LSST if a Bayesian prior probability and a calibration sample are used.