ELSA -- Enhanced latent spaces for improved collider simulations

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 RAMBO-on-diet mapping. We find that modified simulations can achieve sub-percent precision across a wide range of phase space.

17 pages, 9 figures, 2 tables, code and data at https://github.com/ramonpeter/elsa, v2: journal version

References in corpus (5)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• LHAPDF6: parton density access in the LHC precision era
• Reweighting with Boosted Decision Trees
• Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
• Measurement of the differential cross sections for the associated production of a W boson and jets in proton-proton collisions at sqrt(s) = 13 TeV

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• Precision-Machine Learning for the Matrix Element Method
• Normalizing Flows for High-Dimensional Detector Simulations
• Calorimeter shower superresolution
• Differentiable MadNIS-Lite
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• Extrapolating Jet Radiation with Autoregressive Transformers