2021/08/30 by Lukas Pfahler, Pfahler, Lukas, Mirko Bunse +3
Computer Science · Physics and Astronomy · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #astro-ph.IM #cs.LG
paper · pdf · doi:10.48550/arxiv.2108.13396
arxiv created 2021/08/30 · openalex publication_date 2021/08/30 · arxiv updated 2021/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Gamma hadron classification, a central machine learning task in gamma ray astronomy, is conventionally tackled with supervised learning. However, the supervised approach requires annotated training data to be produced in sophisticated and costly simulations. We propose to instead solve gamma hadron classification with a noisy label approach that only uses unlabeled data recorded by the real telescope. To this end, we employ the significance of detection as a learning criterion which addresses this form of weak supervision. We show that models which are based on the significance of detection deliver state-of-the-art results, despite being exclusively trained with noisy labels; put differently, our models do not require the costly simulated ground-truth labels that astronomers otherwise employ for classifier training. Our weakly supervised models exhibit competitive performances also on imbalanced data sets that stem from a variety of other application domains. In contrast to existing work on class-conditional label noise, we assume that only one of the class-wise noise rates is known.