Bayesian classification of astronomical spectra with class uncertainties
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 4...
| Authors | Simon Barton, Martin Sahlén, Andreas Korn et al. |
|---|---|
| Primary category | astro-ph.IM |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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