Generators and the (Accelerated) Future
arXiv:2202.05991 · doi:10.1088/1742-6596/2438/1/012001
Abstract
With the High Luminosity LHC coming online in the near future, event generators will need to provide very large event samples to match the experimental precision. Currently, the estimated cost to generate these events exceeds the computing budget of the LHC experiments. To address these issues, the computing efficiency of event generators need to be improved. Many different approaches are being taken to achieve this goal. I will cover the ongoing work on implementing event generators on the GPUs, machine learning the matrix element, machine learning the phase space, and minimizing the number of negative weight events.
arXiv comment11 pages, 8 figures, 5 tables, plenary proceedings for the 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research
References in corpus (35)
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- MadGraph 5 : Going Beyond
- ALPGEN, a generator for hard multiparton processes in hadronic collisions
- A General Algorithm for Calculating Jet Cross Sections in NLO QCD
- UFO - The Universal FeynRules Output
- MadGraph/MadEvent v4: The New Web Generation
- Scattering Amplitudes with Open Loops
- Comix, a new matrix element generator
- MadEvent: Automatic Event Generation with MadGraph
- SARAH 4: A tool for (not only SUSY) model builders
- CutTools: a program implementing the OPP reduction method to compute one-loop amplitudes
- General-purpose event generators for LHC physics
- AMEGIC++ 1.0, A Matrix Element Generator In C++
- GoSam-2.0: a tool for automated one-loop calculations within the Standard Model and beyond
- Helac-nlo
- RECOLA: REcursive Computation of One-Loop Amplitudes
- Automating dipole subtraction for QCD NLO calculations
- A comprehensive approach to new physics simulations
- HELAC: a package to compute electroweak helicity amplitudes
- How to GAN LHC Events
- PHEGAS: a phase-space generator for automatic cross-section computation
- i-flow: High-dimensional Integration and Sampling with Normalizing Flows
- Exploring phase space with Neural Importance Sampling
- DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
- Neural Network-Based Approach to Phase Space Integration
- GANplifying Event Samples
- A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
- A factorisation-aware Matrix element emulator
- Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
- Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
- Lund jet images from generative and cycle-consistent adversarial networks
- Improved Neural Network Monte Carlo Simulation
- Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs
- A Positive Resampler for Monte Carlo Events with Negative Weights
- MadFlow: automating Monte Carlo simulation on GPU for particle physics processes