Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Reliable labels are costly, while community labels can be noisy and survey-dependent. We aim to develop a Real-Bogus classification framework that can be trained without human-labeled data using injected transients and bogus-dominated survey data, remains robust under strong class contamination, and provides calibrated uncertainty quantification. We combine simulated transient injections with a contaminated survey class and train a dual-network model using asymmetric co-teaching for classes with different label-noise levels. We evaluate performance on ...
| Authors | Raphaël Bonnet-Guerrini, Bruno Sanchez, Dominique Fouchez et al. |
|---|---|
| Primary category | astro-ph.IM |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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