Metraipolis › archive › dc_676aeeb22efc

Bayesian classification of astronomical spectra with class uncertainties — arXiv:2609.21694

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21694
License (as archived): CC0-1.0
Archived: 2026-10-10T01:47:34.807Z
SHA-256 of the archived text: 2b66177ae5f0540433e5f73dce760a37091dc7cc5b8d3a6c75ca6d5107776edc
Stable URL of this page: https://metraipolis.com/documento/dc_676aeeb22efc

How to cite

Bayesian classification of astronomical spectra with class uncertainties — arXiv:2609.21694 — arXiv (en), https://arxiv.org/abs/2609.21694 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_676aeeb22efc (Metraipolis archive dc_676aeeb22efc, sha256 2b66177ae5f0540433e5f73dce760a37091dc7cc5b8d3a6c75ca6d5107776edc)

BibTeX

@misc{dc676aeeb22efc,
  title = {Bayesian classification of astronomical spectra with class uncertainties — arXiv:2609.21694},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21694},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_676aeeb22efc; sha256 2b66177ae5f0540433e5f73dce760a37091dc7cc5b8d3a6c75ca6d5107776edc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21694. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Context: We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spectra of stellar and extragalactic targets for the upcoming 4MOST survey. In fulfilment of the survey requirements, this method should be able to express uncertainty in the input data as well as uncertainty introduced in its prediction. Aims: Four different methods are explored: (1) convolutional neural networks (CNNs), (2) the Dirichlet distribution, (3) Monte Carlo dropout (MCD), (4) Bayesian neural Networks (BNNs) + variational inference (VI). Training and validation was performed using labelled spectra from the SDSS database and a custom 4MOST mock dataset. All the methods were compared in terms of the same metrics: accuracy, area under the curve (AUC), expected calibration error (ECE), Shannon entropy, negative log-likelihood (NLL), Brier score, training time, and inference time. Methods: A CNN with simple architecture and about 20,000 parameters was trained to achieve classification accuracies of 91.5% on SDSS data and 92.8% on 4MOST mock data. The direct Dirichlet prediction and VI models tested provide uncertainties on class membership probabilities, but they confuse classes more often. The MCD on a CNN is found to be the most suitable; it boosts the point-estimate accuracies to 92.6% and 93.9%, while still providing fast training and sufficiently fast inference. Compared to a standard CNN, the method additionally provides well-calibrated uncertainties at marginal extra cost.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in cs.LG).
Categorie arXiv: Instrumentation and Methods for Astrophysics (astro-ph.IM) ; Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an).
Pagina del lavoro: https://arxiv.org/abs/2609.21694 — 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 2026-10-10T01:14:16.712Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).