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

• URL: http://arxiv.org/abs/2501.06532v1
• Date: Sat, 11 Jan 2025 12:42:07 GMT
• Title: Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)
• Authors: M. Garcia-Fernandez,
• Abstract summary: We present a new algorithmic approach for determining photometric redshifts of galaxies using Conditional Generative Adversarial Networks (CGANs) Proposed CGAN implementation, approaches photometric redshift determination as a probabilistic regression, where instead of determining a single value for the estimated redshift of the galaxy, a full probability density is computed.
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• Abstract: Accurate and reliable photometric redshifts 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 determined. On this paper, we present a new algorithmic approach for determining photometric redshifts of galaxies using Conditional Generative Adversarial Networks (CGANs). Proposed CGAN implementation, approaches photometric redshift determination as a probabilistic regression, where instead of determining a single value for the estimated redshift of the galaxy, a full probability density is computed. The methodology proposed, is tested with data from Dark Energy Survey (DES) Y1 data and compared with other existing algorithm such as a Random Forest regressor.

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