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Title: Elsa: enhanced latent spaces for improved collider simulations

Journal Article · · European Physical Journal. C, Particles and Fields (Online)

Abstract Simulations play a key role for inference in collider physics. We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinement). To clearly illustrate our approaches, we use W + jets matrix element surrogate simulations based on normalizing flows as a prototypical example. First, weights in the data space are derived using machine learning classifiers. Then, we pull back the data-space weights to the latent space to produce unweighted examples and employ the Latent Space Refinement ( Laser ) protocol using Hamiltonian Monte Carlo. An alternative approach is an augmented normalizing flow, which allows for different dimensions in the latent and target spaces. These methods are studied for various pre-processing strategies, including a new and general method for massive particles at hadron colliders that is a tweak on the widely-used RamboOnDiet mapping. We find that modified simulations can achieve sub-percent precision across a wide range of phase space.

Sponsoring Organization:
USDOE
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
2004601
Journal Information:
European Physical Journal. C, Particles and Fields (Online), Journal Name: European Physical Journal. C, Particles and Fields (Online) Journal Issue: 9 Vol. 83; ISSN 1434-6052
Publisher:
Springer Science + Business MediaCopyright Statement
Country of Publication:
Germany
Language:
English

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