{"ID":5936919,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T17:51:18.37832961Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05393","arxiv_id":"2607.05393","title":"Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification","abstract":"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 a benchmark subset and analyze the learned representation with latent-space visualization tools. For uncertainty quantification (UQ), we compare MC dropout and deep ensembles and propose a low-cost hybrid strategy that exploits the dual-network setting to improve calibration. We extend the evaluation to the light-curve domain to assess recovery of light-curve classes. The method achieves strong Real-Bogus performance on the labeled subset and remains stable under severe class contamination. It recovers transient light-curve classes with high fidelity, while single-source identification is limited by ambiguity in light-curve-derived labels. Our hybrid UQ approach achieves competitive calibration relative to more expensive ensemble baselines. Latent-space analyses indicate that uncertainty aligns with the decision boundary and reveal subclasses within the bogus population. Our results show that injection-driven, weakly supervised training can enable scalable and consistent Real-Bogus classification without human-labeled training data while providing calibrated uncertainties. The method is suited for transfer to forthcoming surveys by re-running the injection-based training pipeline.","short_abstract":"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-label...","url_abs":"https://arxiv.org/abs/2607.05393","url_pdf":"https://arxiv.org/pdf/2607.05393v1","authors":"[\"Raphaël Bonnet-Guerrini\",\"Bruno Sanchez\",\"Dominique Fouchez\",\"Benjamin Racine\",\"Maya Guy\",\"Mariam Sabalbal\",\"Manal Yassine\",\"Vincenzo Piuri\"]","published":"2026-07-06T17:59:58Z","proceeding":"astro-ph.IM","tasks":"[\"astro-ph.IM\",\"astro-ph.GA\",\"astro-ph.HE\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}