Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)

TL;DR

This paper introduces a novel approach using Conditional Generative Adversarial Networks (CGANs) for estimating galaxy photometric redshifts, providing both point and probability density estimates, and compares its performance with existing methods.

Contribution

The paper presents a new CGAN-based method for galaxy photometric redshift estimation that can produce both point and probability density estimates, expanding the tools available for astronomical surveys.

Findings

CGANs produce results close to Mixture Density Networks in quality metrics.

The proposed CGAN approach can estimate both point and probability densities.

Results demonstrate potential for CGANs in photometric redshift estimation.

Abstract

Accurate and reliable photometric redshift determination is one of the key aspects for wide-field photometric surveys. Determination of photometric redshift for galaxies, has been traditionally solved by use of machine-learning and artificial intelligence techniques trained on a calibration sample of galaxies, where both photometry and spectrometry are available. On this paper, we present a new algorithmic approach for determining photometric redshifts of galaxies using Conditional Generative Adversarial Networks (CGANs). The proposed implementation is able to determine both point-estimation and probability-density estimations for photometric redshifts. The methodology is tested with data from Dark Energy Survey (DES) Y1 data and compared with other existing algorithm such as a Mixture Density Network (MDN). Although results obtained show a superiority of MDN, CGAN quality-metrics are close to the MDN results, opening the door to the use of CGAN at photometric redshift estimation.