Authors

Abstract

We study the performance of the hybrid template machine learning photometric redshift (photo-z) algorithm delight, which uses Gaussian processes, on a subset of the early data release of the Physics of the Accelerating Universe Survey (PAUS). We calibrate the fluxes of the 40 PAUS narrow bands with six broad-band fluxes (uBVriz) in the Cosmic Evolution Survey (COSMOS) field using three different methods, including a new method that utilizes the correlation between the apparent size and overall flux of the galaxy. We use a rich set of empirically derived galaxy spectral templates as guides to train the Gaussian process, and we show that our results are competitive with other standard photometric redshift algorithms. delight achieves a photo-z 68th percentile error of sigma(68) = 0.0081(1 + z) without any quality cut for galaxies with i(auto) < 22.5 as compared to 0.0089(1 + z) and 0.0202(1 + z) for the BPZ and ANNZ2 codes, respectively. delight is also shown to produce more accurate probability distribution functions for individual redshift estimates than BPZ and ANNZ2. Common photo-z outliers of delight and BCNZ2 (previously applied to PAUS) are found to be primarily caused by outliers in the narrow-band fluxes, with a small number of cases potentially indicating spectroscopic redshift failures in the reference sample. In the process, we introduce performance metrics derived from the results of BCNZ2 and delight, allowing us to achieve a photo-z quality of sigma(68) < 0.0035(1 + z) at a magnitude of i(auto) < 22.5 while keeping 50 per cent objects of the galaxy sample.