Advancing Tools for Simulation-Based Inference

Autor:	Bahl, Henning, Bresó, Victor, De Crescenzo, Giovanni, Plehn, Tilman
Rok vydání:	2024
Předmět:	High Energy Physics - Phenomenology High Energy Physics - Experiment
Druh dokumentu:	Working Paper
Popis:	We study the benefit of modern simulation-based inference to constrain particle interactions at the LHC. We explore ways to incorporate known physics structures into likelihood estimation, specifically morphing-aware estimation and derivative learning. Technically, we introduce a new and more efficient smearing algorithm, illustrate how uncertainties can be approximated through repulsive ensembles, and show how equivariant networks can improve likelihood estimation. After illustrating these aspects for a toy model, we target di-boson production at the LHC and find that our improvements significantly increase numerical control and stability. Comment: 25 pages, 13 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.07315 Zobrazit plný text záznamu View this record from Arxiv