Loss function to optimise signal significance in particle physics

Autor:	Bardhan, Jai, Neeraj, Cyrin, Mitra, Subhadip, Mandal, Tanumoy
Rok vydání:	2024
Předmět:	High Energy Physics - Phenomenology Computer Science - Machine Learning High Energy Physics - Experiment
Druh dokumentu:	Working Paper
Popis:	We construct a surrogate loss to directly optimise the significance metric used in particle physics. We evaluate our loss function for a simple event classification task using a linear model and show that it produces decision boundaries that change according to the cross sections of the processes involved. We find that the models trained with the new loss have higher signal efficiency for similar values of estimated signal significance compared to ones trained with a cross-entropy loss, showing promise to improve sensitivity of particle physics searches at colliders. Comment: 9 pages, 4 figures. Appeared in the Machine Learning for Physical Sciences (ML4PS) workshop in NeurIPS 2024 conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.09500 Zobrazit plný text záznamu View this record from Arxiv