Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

Abstract

The generation of unit-weight events for complex scattering processes presents a severe challenge to modern Monte Carlo event generators. Even when using sophisticated phase-space sampling techniques adapted to the underlying transition matrix elements, the efficiency for generating unit-weight events from weighted samples can become a limiting factor in practical applications. Here we present a novel two-staged unweighting procedure that makes use of a neural-network surrogate for the full event weight. The algorithm can significantly accelerate the unweighting process, while it still guarantees unbiased sampling from the correct target distribution. We apply, validate and benchmark the new approach in high-multiplicity LHC production processes, including $Z/W$+4 jets and $t\bar{t}$+3 jets, where we find speed-up factors up to ten.

37 pages, 9 figures, version accepted for publication in SciPost Physics

References in corpus (18)

• PYTHIA 6.4 Physics and Manual
• 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
• Parton distributions for the LHC
• Event generation with SHERPA 1.1
• NICE: Non-linear Independent Components Estimation
• Robust Independent Validation of Experiment and Theory: Rivet version 3
• Recola2: REcursive Computation of One-Loop Amplitudes 2
• aMCfast: automation of fast NLO computations for PDF fits
• On the maximal use of Monte Carlo samples: re-weighting events at NLO accuracy
• Understanding Event-Generation Networks via Uncertainties
• A factorisation-aware Matrix element emulator
• Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
• Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
• Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks
• Exhaustive Neural Importance Sampling applied to Monte Carlo event generation
• (MC)**3 -- a Multi-Channel Markov Chain Monte Carlo algorithm for phase-space sampling
• Accelerating LHC phenomenology with analytic one-loop amplitudes: A C++ interface to MCFM

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