The PAU Survey: Enhancing photometric redshift estimation using DEEPz

TL;DR

This paper introduces DEEPz, a deep-learning method for photometric redshift estimation, applied to the PAUS survey, demonstrating improved accuracy over traditional methods and effective identification of close galaxy pairs.

Contribution

The paper presents a novel deep-learning approach, DEEPz, that enhances photometric redshift accuracy and galaxy pair identification in large surveys.

Findings

DEEPz achieves 20-50% smaller scatter than BCNz2 for faint galaxies.

Combining W1+W3 samples improves redshift precision.

DEEPz effectively identifies close galaxy pairs within specified redshift and magnitude ranges.

Abstract

We present photometric redshifts for 1 341 559 galaxies from the Physics of the Accelerating Universe Survey (PAUS) over 50.38 ${\rm deg}^{2}$ of sky to $i_{\rm AB}=23$. Redshift estimation is performed using DEEPz, a deep-learning photometric redshift code. We analyse the photometric redshift precision when varying the photometric and spectroscopic samples. Furthermore, we examine observational and instrumental effects on the precision of the photometric redshifts, and we compare photometric redshift measurements with those obtained using a template method-fitting BCNz2. Finally, we examine the use of photometric redshifts in the identification of close galaxy pairs. We find that the combination of samples from W1+W3 in the training of DEEPz significantly enhances the precision of photometric redshifts. This also occurs when we recover narrow band fluxes using broad bands measurements. We show that DEEPz determines the redshifts of galaxies in the prevailing spectroscopic catalogue used in the training of DEEPz with greater precision. For the faintest galaxies ($i_{\rm AB}=21-23$), we find that DEEPz improves over BCNz2 both in terms of the precision (20-50 per cent smaller scatter) and in returning a smaller outlier fraction in two of the wide fields. The catalogues were tested for the identification of close galaxy pairs, showing that DEEPz is effective for the identification of close galaxy pairs for samples with $i_{\rm AB} < 22.5$ and redshift $0.2 < z < 0.6$. In addition, identifying close galaxy pairs common between DEEPz and BCNz2 is a promising approach to improving the purity of the catalogues of these systems.