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

M. Garcia-Fernandez

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arXiv · 2501.06532

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

M. Garcia-Fernandez

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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.

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M. Garcia-Fernandez. 2025-01-11. Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs). https://arxiv.org/abs/2501.06532

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