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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.06532 |
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| _version_ | 1866908453296930816 |
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| author | Garcia-Fernandez, M. |
| author_facet | Garcia-Fernandez, M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06532 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs) Garcia-Fernandez, M. Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Artificial Intelligence 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. |
| title | Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs) |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Artificial Intelligence |
| url | https://arxiv.org/abs/2501.06532 |