Reweighting a parton shower using a neural network: the final-state case

Abstract

The use of QCD calculations that include the resummation of soft-collinear logarithms via parton-shower algorithms is currently not possible in PDF fits due to the high computational cost of evaluating observables for each variation of the PDFs. Unfortunately the interpolation methods that are otherwise applied to overcome this issue are not readily generalised to all-order parton-shower contributions. Instead, we propose an approximation based on training a neural network to predict the effect of varying the input parameters of a parton shower on the cross section in a given observable bin, interpolating between the variations of a training data set. This first publication focuses on providing a proof-of-principle for the method, by varying the shower dependence on ${\alpha }_{\text{S}}$ for both a simplified shower model and a complete shower implementation for three different observables, the leading emission scale, the number of emissions and the Thrust event shape. The extension to the PDF dependence of the initial-state shower evolution that is needed for the application to PDF fits is left to a forthcoming publication.

arXiv commentadditional references added in introduction

References in corpus (32)

• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• Parton distributions for the LHC Run II
• Event generation with SHERPA 1.1
• LHAPDF6: parton density access in the LHC precision era
• Parton distributions from high-precision collider data
• Parton distributions in the LHC era: MMHT 2014 PDFs
• A parton shower algorithm based on Catani-Seymour dipole factorisation
• Rivet user manual
• A critical appraisal of NLO+PS matching methods
• Next-to-leading order calculation of three-jet observables in hadron-hadron collision
• General-purpose event generators for LHC physics
• Classification without labels: Learning from mixed samples in high energy physics
• Deep learning in color: towards automated quark/gluon jet discrimination
• A posteriori inclusion of parton density functions in NLO QCD final-state calculations at hadron colliders: The APPLGRID Project
• Jet-Images -- Deep Learning Edition
• The Structure of the Proton in the LHC Precision Era
• Deep-learned Top Tagging with a Lorentz Layer
• Four-lepton production at hadron colliders: aMC@NLO predictions with theoretical uncertainties
• Matrix-Element Corrections to Parton Shower Algorithms
• Pulling Out All the Tops with Computer Vision and Deep Learning
• Pileup Mitigation with Machine Learning (PUMML)
• Ntuples for NLO Events at Hadron Colliders
• Reweighting QCD matrix-element and parton-shower calculations
• fastNLO: Fast pQCD Calculations for PDF Fits
• aMCfast: automation of fast NLO computations for PDF fits
• Boosting $H\to b\bar{b}$ with Machine Learning
• Recursive Neural Networks in Quark/Gluon Tagging
• Fooling Around with the Sudakov Veto Algorithm
• MCgrid: projecting cross section calculations on grids
• MINLO t-channel single-top plus jet

Cited by in corpus (34)

• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Robust Independent Validation of Experiment and Theory: Rivet version 3
• How to GAN LHC Events
• OmniFold: A Method to Simultaneously Unfold All Observables
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Machine Learning and LHC Event Generation
• Invertible Networks or Partons to Detector and Back Again
• Neural Networks for Full Phase-space Reweighting and Parameter Tuning
• How to GAN away Detector Effects
• Understanding Event-Generation Networks via Uncertainties
• Event Generators for High-Energy Physics Experiments
• Neural Network-Based Approach to Phase Space Integration
• Generative Networks for Precision Enthusiasts
• GANplifying Event Samples
• A factorisation-aware Matrix element emulator
• Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
• Targeting Multi-Loop Integrals with Neural Networks
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• How to Understand Limitations of Generative Networks
• Two Invertible Networks for the Matrix Element Method
• The MadNIS Reloaded
• Probing triple Higgs coupling with machine learning at the LHC
• One-loop matrix element emulation with factorisation awareness
• How to GAN Event Subtraction
• High dimensional parameter tuning for event generators
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Precision-Machine Learning for the Matrix Element Method
• ELSA -- Enhanced latent spaces for improved collider simulations
• Normalizing Flows for High-Dimensional Detector Simulations
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Differentiable MadNIS-Lite
• OASIS: Optimal Analysis-Specific Importance Sampling for event generation
• How to GAN Event Unweighting
• BitHEP -- The Limits of Low-Precision ML in HEP