Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows

Abstract

We explore the use of autoregressive flows, a type of generative model with tractable likelihood, as a means of efficient generation of physical particle collider events. The usual maximum likelihood loss function is supplemented by an event weight, allowing for inference from event samples with variable, and even negative event weights. To illustrate the efficacy of the model, we perform experiments with leading-order top pair production events at an electron collider with importance sampling weights, and with next-to-leading-order top pair production events at the LHC that involve negative weights.

26 pages, 7 figures

References in corpus (12)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• Matching NLO QCD computations with Parton Shower simulations: the POWHEG method
• Event generation with SHERPA 1.1
• Herwig++ Physics and Manual
• Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations
• OneLOop: for the evaluation of one-loop scalar functions
• Parton showers with more exact color evolution
• Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
• Cherenkov Detectors Fast Simulation Using Neural Networks
• Exhaustive Neural Importance Sampling applied to Monte Carlo event generation
• Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model