SciPost Physics

SciPost Phys. 7, 075 (2019) · published 4 December 2019

• doi: 10.21468/SciPostPhys.7.6.075
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Abstract

Event generation for the LHC can be supplemented by generative adversarial networks, which generate physical events and avoid highly inefficient event unweighting. For top pair production we show how such a network describes intermediate on-shell particles, phase space boundaries, and tails of distributions. In particular, we introduce the maximum mean discrepancy to resolve sharp local features. It can be extended in a straightforward manner to include for instance off-shell contributions, higher orders, or approximate detector effects.

• Chisholm et al., Non-parametric data-driven background modelling using conditional probabilities
  J. High Energ. Phys. 2022, 1 (2022) [Crossref]
• Badger et al., Loop amplitudes from precision networks
  SciPost Phys. Core 6, 034 (2023) [Crossref]
• Lifson et al., Calculating the primary Lund Jet Plane density
  J. High Energ. Phys. 2020, 170 (2020) [Crossref]
• Lu et al., Resolving extreme jet substructure
  J. High Energ. Phys. 2022, 46 (2022) [Crossref]
• Das et al., How to understand limitations of generative networks
  SciPost Phys. 16, 031 (2024) [Crossref]
• Ghosh et al., Towards a deep learning model for hadronization
  Phys. Rev. D 106, 096020 (2022) [Crossref]
• Menary et al., Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences
  Mach. Learn.: Sci. Technol. 3, 015021 (2022) [Crossref]
• Bellagente et al., Invertible networks or partons to detector and back again
  SciPost Phys. 9, 074 (2020) [Crossref]
• Velasco et al.,
  1395, 245 (2022) [Crossref]
• Capozi et al., Exploring anomalous couplings in Higgs boson pair production through shape analysis
  J. High Energ. Phys. 2020, 91 (2020) [Crossref]
• Bothmann et al., Exploring phase space with Neural Importance Sampling
  SciPost Phys. 8, 069 (2020) [Crossref]
• Munoz et al., Boost invariant polynomials for efficient jet tagging
  Mach. Learn.: Sci. Technol. 3, 04LT05 (2022) [Crossref]
• Finke et al., Autoencoders for unsupervised anomaly detection in high energy physics
  J. High Energ. Phys. 2021, 161 (2021) [Crossref]
• Butter et al., Generative networks for precision enthusiasts
  SciPost Phys. 14, 078 (2023) [Crossref]
• Carrazza et al., Lund jet images from generative and cycle-consistent adversarial networks
  Eur. Phys. J. C 79, 979 (2019) [Crossref]
• Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
  SciPost Phys. 8, 090 (2020) [Crossref]
• Finke et al., Boosting mono-jet searches with model-agnostic machine learning
  J. High Energ. Phys. 2022, 15 (2022) [Crossref]
• Andreassen et al., Parameter estimation using neural networks in the presence of detector effects
  Phys. Rev. D 103, 036001 (2021) [Crossref]
• Khosa et al., A simple guide from machine learning outputs to statistical criteria in particle physics
  SciPost Phys. Core 5, 050 (2022) [Crossref]
• Bellagente et al., How to GAN away detector effects
  SciPost Phys. 8, 070 (2020) [Crossref]
• Carrazza et al., Compressing PDF sets using generative adversarial networks
  Eur. Phys. J. C 81, 530 (2021) [Crossref]
• Diefenbacher et al., DCTRGAN: improving the precision of generative models with reweighting
  J. Inst. 15, P11004 (2020) [Crossref]
• Butter, Normalizing Flows for LHC Theory
  J. Phys.: Conf. Ser. 2438, 012004 (2023) [Crossref]
• Larkoski, General analysis for observing quantum interference at colliders
  Phys. Rev. D 105, 096012 (2022) [Crossref]
• Badger et al., Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
  J. High Energ. Phys. 2020, 114 (2020) [Crossref]
• Danziger et al., Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates
  SciPost Phys. 12, 164 (2022) [Crossref]
• Chen et al., Improved neural network Monte Carlo simulation
  SciPost Phys. 10, 023 (2021) [Crossref]
• Chen et al., Analysis-Specific Fast Simulation at the LHC with Deep Learning
  Comput Softw Big Sci 5, 15 (2021) [Crossref]
• Dreyer et al., Quarks and gluons in the Lund plane
  J. High Energ. Phys. 2022, 177 (2022) [Crossref]
• Felser et al., Quantum-inspired machine learning on high-energy physics data
  npj Quantum Inf 7, 111 (2021) [Crossref]
• Jinno et al., Machine learning Post-Minkowskian integrals
  J. High Energ. Phys. 2023, 181 (2023) [Crossref]
• Baldi et al., How to GAN Higher Jet Resolution
  SciPost Phys. 13, 064 (2022) [Crossref]
• Wang et al., A novel scenario in the semi-constrained NMSSM
  J. High Energ. Phys. 2020, 78 (2020) [Crossref]
• Tsai et al., Inverting cosmic ray propagation by convolutional neural networks
  J. Cosmol. Astropart. Phys. 2022, 044 (2022) [Crossref]
• Backes et al., How to GAN Event Unweighting
  SciPost Phys. 10, 089 (2021) [Crossref]
• Matchev et al., OASIS: Optimal Analysis-Specific Importance Sampling for event generation
  SciPost Phys. 10, 034 (2021) [Crossref]
• Agarwal et al., Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation
  J. High Energ. Phys. 2021, 208 (2021) [Crossref]
• Maître et al., One-loop matrix element emulation with factorisation awareness
  J. High Energ. Phys. 2023, 159 (2023) [Crossref]
• et al., Deep Generative Models for Fast Photon Shower Simulation in ATLAS
  Comput Softw Big Sci 8, 7 (2024) [Crossref]
• Butter et al., Machine learning and LHC event generation
  SciPost Phys. 14, 079 (2023) [Crossref]
• Aylett-Bullock et al., Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
  J. High Energ. Phys. 2021, 66 (2021) [Crossref]
• Elflein et al., Ultra-fast generation of air shower images for Imaging Air Cherenkov Telescopes using Generative Adversarial Networks
  J. Inst. 19, P04010 (2024) [Crossref]
• Winterhalder et al., Targeting multi-loop integrals with neural networks
  SciPost Phys. 12, 129 (2022) [Crossref]
• Butter et al., Two invertible networks for the matrix element method
  SciPost Phys. 15, 094 (2023) [Crossref]
• Touranakou et al., Particle-based fast jet simulation at the LHC with variational autoencoders
  Mach. Learn.: Sci. Technol. 3, 035003 (2022) [Crossref]
• Bieringer et al., Calomplification — the power of generative calorimeter models
  J. Inst. 17, P09028 (2022) [Crossref]
• Pata et al., MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks
  Eur. Phys. J. C 81, 381 (2021) [Crossref]
• Nachman et al., Anomaly detection with density estimation
  Phys. Rev. D 101, 075042 (2020) [Crossref]
• Bellagente et al., Understanding Event-Generation Networks via Uncertainties
  SciPost Phys. 13, 003 (2022) [Crossref]
• Kansal et al., Evaluating generative models in high energy physics
  Phys. Rev. D 107, 076017 (2023) [Crossref]
• Kasieczka et al., The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics
  Rep. Prog. Phys. 84, 124201 (2021) [Crossref]
• Stienen et al., Phase space sampling and inference from weighted events with autoregressive flows
  SciPost Phys. 10, 038 (2021) [Crossref]
• Isaacson, Generators and the (Accelerated) Future
  J. Phys.: Conf. Ser. 2438, 012001 (2023) [Crossref]
• Gao et al., Event generation with normalizing flows
  Phys. Rev. D 101, 076002 (2020) [Crossref]
• Maddrell-Mander et al., Studying the Potential of Graphcore® IPUs for Applications in Particle Physics
  Comput Softw Big Sci 5, 8 (2021) [Crossref]
• Velasco et al.,
  , 372 (2020) [Crossref]
• Buhmann et al., CaloClouds II: ultra-fast geometry-independent highly-granular calorimeter simulation
  J. Inst. 19, P04020 (2024) [Crossref]
• Leigh et al., Faster diffusion model with improved quality for particle cloud generation
  Phys. Rev. D 109, 012010 (2024) [Crossref]
• Bhattacherjee et al., Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network
  J. High Energ. Phys. 2019, 156 (2019) [Crossref]
• Soybelman et al., Set-conditional set generation for particle physics
  Mach. Learn.: Sci. Technol. 4, 045036 (2023) [Crossref]
• Araz et al., Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States
  J. High Energ. Phys. 2021, 112 (2021) [Crossref]
• Maître et al., Multi-variable integration with a neural network
  J. High Energ. Phys. 2023, 221 (2023) [Crossref]
• Shyamsundar et al., Variance reduction via simultaneous importance sampling and control variates techniques using vegas
  SciPost Phys. Codebases, 28 (2024) [Crossref]
• Matchev et al., Uncertainties associated with GAN-generated datasets in high energy physics
  SciPost Phys. 12, 104 (2022) [Crossref]
• Krause et al., Fast and accurate simulations of calorimeter showers with normalizing flows
  Phys. Rev. D 107, 113003 (2023) [Crossref]
• Hammad et al., Riemannian data preprocessing in machine learning to focus on QCD color structure
  J. Korean Phys. Soc. 83, 235 (2023) [Crossref]
• Butter et al., How to GAN event subtraction
  SciPost Phys. Core 3, 009 (2020) [Crossref]
• Fraser et al., Challenges for unsupervised anomaly detection in particle physics
  J. High Energ. Phys. 2022, 66 (2022) [Crossref]
• Chakraborty et al., Neural network-based top tagger with two-point energy correlations and geometry of soft emissions
  J. High Energ. Phys. 2020, 111 (2020) [Crossref]
• Doglioni et al., Physics Inspired Deep Neural Networks for Top Quark Reconstruction
  EPJ Web Conf. 245, 06029 (2020) [Crossref]
• Kuttan et al., An equation-of-state-meter for CBM using PointNet
  J. High Energ. Phys. 2021, 184 (2021) [Crossref]
• Chen et al., Deep learning jet substructure from two-particle correlations
  Phys. Rev. D 101, 114025 (2020) [Crossref]
• Nachman et al., Neural resampler for Monte Carlo reweighting with preserved uncertainties
  Phys. Rev. D 102, 076004 (2020) [Crossref]
• Gao et al., i- flow: High-dimensional integration and sampling with normalizing flows
  Mach. Learn.: Sci. Technol. 1, 045023 (2020) [Crossref]
• Dillon et al., Learning the latent structure of collider events
  J. High Energ. Phys. 2020, 206 (2020) [Crossref]
• Deutschmann et al., Accelerating HEP simulations with Neural Importance Sampling
  J. High Energ. Phys. 2024, 83 (2024) [Crossref]
• Bravo-Prieto et al., Style-based quantum generative adversarial networks for Monte Carlo events
  Quantum 6, 777 777 (2022) [Crossref]
• Alghamdi et al., Toward a generative modeling analysis of CLAS exclusive 2π photoproduction
  Phys. Rev. D 108, 094030 (2023) [Crossref]
• Howard et al., Learning to simulate high energy particle collisions from unlabeled data
  Sci Rep 12, 7567 (2022) [Crossref]
• Xu et al., Generative machine learning for detector response modeling with a conditional normalizing flow
  J. Inst. 19, P02003 (2024) [Crossref]
• Alanazi et al., Machine learning-based event generator for electron-proton scattering
  Phys. Rev. D 106, 096002 (2022) [Crossref]
• Leigh et al., PC-JeDi: Diffusion for particle cloud generation in high energy physics
  SciPost Phys. 16, 018 (2024) [Crossref]
• Butter et al., GANplifying event samples
  SciPost Phys. 10, 139 (2021) [Crossref]
• Amram et al., Tag N’ Train: a technique to train improved classifiers on unlabeled data
  J. High Energ. Phys. 2021, 153 (2021) [Crossref]
• Maître et al., A factorisation-aware Matrix element emulator
  J. High Energ. Phys. 2021, 66 (2021) [Crossref]
• Lai et al., Explainable machine learning of the underlying physics of high-energy particle collisions
  Physics Letters B 829, 137055 137055 (2022) [Crossref]
• Mehtar-Tani et al., Tagging boosted hadronic objects with dynamical grooming
  Phys. Rev. D 102, 114013 (2020) [Crossref]
• Buhmann et al., Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
  Comput Softw Big Sci 5, 13 (2021) [Crossref]
• Ba et al., SHAPER: can you hear the shape of a jet?
  J. High Energ. Phys. 2023, 195 (2023) [Crossref]
• et al., Challenges in Monte Carlo Event Generator Software for High-Luminosity LHC
  Comput Softw Big Sci 5, 12 (2021) [Crossref]
• Yallup et al., Exploring phase space with nested sampling
  Eur. Phys. J. C 82, 678 (2022) [Crossref]
• Nachman et al., Elsa: enhanced latent spaces for improved collider simulations
  Eur. Phys. J. C 83, 843 (2023) [Crossref]
• Biscarat et al., End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data
  EPJ Web Conf. 251, 04030 (2021) [Crossref]
• Delgado et al., Unsupervised quantum circuit learning in high energy physics
  Phys. Rev. D 106, 096006 (2022) [Crossref]