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Advancing Tools for Simulation-Based Inference
- Publication Year :
- 2024
-
Abstract
- 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.<br />Comment: 25 pages, 13 figures
- Subjects :
- High Energy Physics - Phenomenology
High Energy Physics - Experiment
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2410.07315
- Document Type :
- Working Paper