Unsupervised Discovery of High-Redshift Galaxy Populations with Variational Autoencoders
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918191121301504 |
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| author | Saxena, Aayush |
| author_facet | Saxena, Aayush |
| contents | We apply variational autoencoders to automatically discover galaxy populations using publicly available high-redshift \textit{JWST} spectra without prior classification knowledge. Our unsupervised method identifies distinct astrophysical classes of unique and exciting galaxy types, demonstrating automated discovery capabilities for large spectroscopic surveys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05439 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised Discovery of High-Redshift Galaxy Populations with Variational Autoencoders Saxena, Aayush Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics We apply variational autoencoders to automatically discover galaxy populations using publicly available high-redshift \textit{JWST} spectra without prior classification knowledge. Our unsupervised method identifies distinct astrophysical classes of unique and exciting galaxy types, demonstrating automated discovery capabilities for large spectroscopic surveys. |
| title | Unsupervised Discovery of High-Redshift Galaxy Populations with Variational Autoencoders |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2511.05439 |