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The work builds a weakly supervised framework trained without human-labeled data using physically motivated injected transients and bogus-dominated survey data. It uses an asymmetric co-teaching dual-network strategy under strong class contamination and delivers calibrated uncertainty quantification for robust downstream decisions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/interpretable-human-label-free-deep-learning-for-real-bogus-classification-with-uncertainty-quantification-research-paper/328040/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/interpretable-human-label-free-deep-learning-for-real-bogus-classification-with-uncertainty-quantification-research-paper/328040.png","ImageObject",300,407,{"name":92,"@type":93},"Eden","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is Real-Bogus classification critical for time-domain surveys like LSST?","Question",{"text":112,"@type":113},"Real-Bogus discrimination is needed to suppress spurious DIA detections early so pipelines can prioritize true candidates before more expensive light-curve analysis.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed method avoid relying on human-labeled training data?",{"text":117,"@type":113},"It trains on physically motivated injected transients and bogus-dominated survey data using a weakly supervised dual-network framework rather than human-labeled examples.",{"name":119,"@type":110,"acceptedAnswer":120},"What approach is used for uncertainty quantification, and how is it evaluated?",{"text":121,"@type":113},"It compares standard UQ methods (MC dropout and deep ensembles) and introduces a low-cost hybrid strategy leveraging the dual-network setting; evaluation includes calibration comparisons to more expensive ensemble baselines.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},328040,1790305679,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":31,"preview_page_count":4},1374391974468,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Astronomy & Astrophysics manuscript no. main ©ESO 2026  \nJuly 7, 2026  \n.05393v1 [ astro-ph .IM] 6 Jul 2026  \nInterpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification  \nRaphaël Bonnet-Guerrini 1, 2 , Bruno Sanchez2 , Dominique Fouchez2 , Benjamin Racine2 , Maya Guy3, 4 , Mariam  \nSabalbal5 , Manal Yassine2 , and Vincenzo Piuri 1  \n1 Università degli Studi di Milano, Department of Computer Science, Milan 20133, Italy e-mail: [raphael.bonnet-guerrini@unimi.it](raphael.bonnet-guerrini@unimi.it) ⋆  \n2 Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France  \n3 Université Côte d’Azur, INRIA, CNRS, Laboratoire J.A.Dieudonné, Maasai team, Nice 06000, France  \n4 Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, Nice 06000 France  \n5 Université de Liège, STAR Institute, Liège 4000, Belgium  \nABSTRACT  \nContext. Time-domain surveys generate large numbers of transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Obtaining reliable human-labeled training sets is costly, while community-provided labels can be noisy and survey-dependent.  \nAims. We aim to develop a Real-Bogus classification framework that can be trained without human-labeled data using physically motivated injected transients and bogus-dominated survey data, that remains robust under strong class contamination, and that provides well-calibrated and reliable uncertainty quantification suitable for downstream decision-making.  \nMethods. We combine simulated transient injections with a heavily contaminated survey class and train a dual-network model using an asymmetric co-teaching strategy for classes with different label-noise levels. We evaluate performance on a labeled benchmark subset and analyze the learned representation with latent-space visualization tools. For uncertainty quantification (UQ), we compare standard approaches (MC dropout and deep ensembles) and propose a low-cost hybrid strategy that exploits the dual-network setting to improve calibration. We further extend the evaluation to the light-curve domain to assess recovery of light-curve classes.  \nResults. 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 fundamentally limited by intrinsic ambiguity in light-curve-derived labels. Our hybrid UQ approach achieves competitive calibration relative to substantially more expensive ensemble baselines. Latent-space analyses indicate that uncertainty aligns with the decision boundary and reveal that the model is able to identify subclasses within the bogus population.  \nConclusions. 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 methodology is well suited for transfer to forthcoming surveys by re-running the injection-based training pipeline.  \nKey words. Astronomical instrumentation, methods and techniques-Methods: data analysis-Methods: statistical  \narXiv:2607  \n1. Introduction  \nThe new Vera C. Rubin Legacy Survey of Space and Time (LSST) (Abell et al. 2009 ; Ivezi et al. 2019) features a time-domain component that will detect transients throughout its 10-year run using Difference Image Analysis (DIA) LSST Science Collaboration et al. (2009); Ivezi et al. (2019); Liu et al. (2024) . In DIA, a template image is subtracted from a newly observed image to identify changes associated with new or variable sources (Alard & Lupton 1998 ; Alard 2000) . This method is crucial for detecting transient events in crowded fields or under varying observational conditions. Typically, supernovae occupy the short-duration region of the luminosity-timescale phase space and therefore benefit from high-cadence observations","cbCaipmTFwFUJStk","https://ap.wps.com/l/cbCaipmTFwFUJStk","pdf",8691183,"English","# 1. Introduction\n## Difference Image Analysis and bogus sources\n## Need for early transient-bogus discrimination\n# Abstract\n## Context, Aims, Methods, Results, Conclusions","[{\"question\":\"Why is Real-Bogus classification critical for time-domain surveys like LSST?\",\"answer\":\"Real-Bogus discrimination is needed to suppress spurious DIA detections early so pipelines can prioritize true candidates before more expensive light-curve analysis.\"},{\"question\":\"How does the proposed method avoid relying on human-labeled training data?\",\"answer\":\"It trains on physically motivated injected transients and bogus-dominated survey data using a weakly supervised dual-network framework rather than human-labeled examples.\"},{\"question\":\"What approach is used for uncertainty quantification, and how is it evaluated?\",\"answer\":\"It compares standard UQ methods (MC dropout and deep ensembles) and introduces a low-cost hybrid strategy leveraging the dual-network setting; evaluation includes calibration comparisons to more expensive ensemble baselines.\"}]","Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification - Research paper | PDF",1789978568]