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FOMO-Z. Optimal galaxy classification, photometric redshifts, and reconstruction of population distributions — arXiv:2610.40875

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40875
License (as archived): CC0-1.0
Archived: 2026-11-02T05:11:37.855Z
SHA-256 of the archived text: 5c9256d963e1bbca7ea777ae46bf90c427f4f8f3937d80de54425e750f754241
Stable URL of this page: https://metraipolis.com/documento/dc_a014df99d548

How to cite

FOMO-Z. Optimal galaxy classification, photometric redshifts, and reconstruction of population distributions — arXiv:2610.40875 — arXiv (en), https://arxiv.org/abs/2610.40875 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a014df99d548 (Metraipolis archive dc_a014df99d548, sha256 5c9256d963e1bbca7ea777ae46bf90c427f4f8f3937d80de54425e750f754241)

BibTeX

@misc{dca014df99d548,
  title = {FOMO-Z. Optimal galaxy classification, photometric redshifts, and reconstruction of population distributions — arXiv:2610.40875},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40875},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a014df99d548; sha256 5c9256d963e1bbca7ea777ae46bf90c427f4f8f3937d80de54425e750f754241},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40875. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We present FOMO-Z (Flexible Optimizer of Multi-dimensional Operators and photo-$z$s), a set of statistical methods implemented in Python for galaxy classification and probabilistic inference from photometry. The methods use a reference sample with known galaxy properties. They rely on explicit, physically interpretable criteria and make no assumptions about the functional form of the color distributions. Binary selections are calibrated using either linear color conditions, particularly suited to background galaxy selection, or flexible color--color grids, seeking to maximize completeness given a threshold on purity. For probabilistic inference, the code identifies reference objects with compatible colors within photometric uncertainties to estimate each test galaxy's probability of satisfying user-defined criteria, as well as probability density functions of continuous properties such as redshift. The code also reconstructs property distributions of selected galaxy populations using photometrically similar galaxies in the reference sample. This framework supports prior weights and reference redshift probability distributions. A built-in feature-selection method identifying informative colors is also introduced. We demonstrate applications to background galaxy selection, galaxy population classification, tomographic redshift calibration, and photometric redshift estimation. The code achieves results consistent with those obtained through machine-learning approaches, such as self-organizing maps, while remaining computationally efficient, making it suitable for large photometric surveys and future Stage IV cosmological analyses. The code is publicly available at this https URL .

Tema degli esploratori: fisica e astronomia (elenco arXiv astro-ph.CO, nuovi lavori del giorno).
Categorie arXiv: Cosmology and Nongalactic Astrophysics (astro-ph.CO) ; Astrophysics of Galaxies (astro-ph.GA).
Pagina del lavoro: https://arxiv.org/abs/2610.40875 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T04:54:58.123Z dalla pagina https://arxiv.org/list/astro-ph.CO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).