Photometry-only classification of SNe Ia and Ibc reaches >=90% accuracy by fitting a semi-analytical decay model to light curves and using GMMs on the resulting parameter distributions to estimate mixing fractions without any labeled training data.
Modeling the probability distribution for cosmological analysis with photometrically classified samples
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abstract
In this work we investigated methods for the accurate and efficient incorporation of photometrically classified supernovae into cosmological analyses, and to assess the impact of the additional uncertainty associated with this procedure on the ability of Type Ia supernovae (SNeIa) tests to place constraints on cosmological models. We proposed a simplified likelihood, in which the contamination is described as a redshift dependent change in the mean of the usually assumed Gaussian distribution, and we tested this hypothesis against the usual two-component approach, based on the BEAMS framework. Using the latest version of the DES supernova sample, dubbed DES-Dovekie, we compared the results when using type probabilities from different classifiers, such as SNIRF and SCONE, and applying different cuts on these probabilities. We show that the new model is strongly favored by the Bayes factor, when compared with the current one, for all configurations, allowing an improvement on the constraining power of photometric supernova data.
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astro-ph.IM 1years
2026 1verdicts
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Photometry is all you need: supernova classification as a mixing problem
Photometry-only classification of SNe Ia and Ibc reaches >=90% accuracy by fitting a semi-analytical decay model to light curves and using GMMs on the resulting parameter distributions to estimate mixing fractions without any labeled training data.