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arXiv · 2410.07315

Advancing Tools for Simulation-Based Inference

Henning Bahl·Victor Bresó·Giovanni De Crescenzo·Tilman Plehn
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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.

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Henning Bahl, Victor Bresó, Giovanni De Crescenzo, Tilman Plehn. 2024-10-09. Advancing Tools for Simulation-Based Inference. https://doi.org/10.21468/scipostphyscore.8.3.060

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