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

• URL: http://arxiv.org/abs/2501.06532v2
• Date: Thu, 13 Mar 2025 12:31:03 GMT
• Title: Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)
• Authors: M. Garcia-Fernandez,
• Abstract summary: Conditional Generative Adversarial Networks (CGANs) are proposed to determine photometric redshifts of galaxies.<n>CGAN quality-metrics are close to the MDN results, opening the door to the use of CGAN at photometric redshift estimation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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