Weakly supervised classifiers trained on background-versus-mixture samples can identify anomalous gamma-ray sources without labeled signal templates, approaching supervised performance in controlled benchmarks.
Hernández-González, I
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2026 1verdicts
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Weakly supervised machine learning for model-agnostic searches of new phenomena in the $\gamma$-ray sky
Weakly supervised classifiers trained on background-versus-mixture samples can identify anomalous gamma-ray sources without labeled signal templates, approaching supervised performance in controlled benchmarks.