A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties

Abstract

Monte Carlo event generators are an essential tool for data analysis in collider physics. To include subleading quantum corrections, these generators often need to produce negative weight events, which leads to statistical dilution of the datasets and downstream computational costs for detector simulation. Building on the recent proposal of a positive resampler method to rebalance weights within histogram bins, we introduce neural resampling: an unbinned approach to Monte Carlo reweighting based on neural networks that scales well to high-dimensional and variable-dimensional phase space. We pay particular attention to preserving the statistical properties of the event sample, such that neural resampling not only maintains the mean value of any observable but also its Monte Carlo uncertainty. This uncertainty preservation scheme is general and can also be applied to binned (non-neural network) resampling. To illustrate our neural resampling approach, we present a case study from the Large Hadron Collider of top quark pair production at next-to-leading order matched to a parton shower.

12 pages, 3 figures, no negative weights; v2: to match journal version

References in corpus (24)

• 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
• OneLOop: for the evaluation of one-loop scalar functions
• Event Generation with Normalizing Flows
• Equivariant flow-based sampling for lattice gauge theory
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Simulation Assisted Likelihood-free Anomaly Detection
• Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
• Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
• i-flow: High-dimensional Integration and Sampling with Normalizing Flows
• Exploring phase space with Neural Importance Sampling
• How to GAN away Detector Effects
• A Positive-Weight Next-to-Leading-Order Monte Carlo for Z Pair Hadroproduction
• Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
• On the reduction of negative weights in MC@NLO-type matching procedures
• Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
• Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks
• LHC analysis-specific datasets with Generative Adversarial Networks
• Cherenkov Detectors Fast Simulation Using Neural Networks
• Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
• Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
• Resonance Searches with Machine Learned Likelihood Ratios
• Efficiency Parameterization with Neural Networks

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