Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters
Abstract
Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theoretical modeling assumptions. Petabytes of simulated data are needed to develop analysis techniques, though they are expensive to generate using existing algorithms and computing resources. The modeling of detectors and the precise description of particle cascades as they interact with the material in the calorimeter are the most computationally demanding steps in the simulation pipeline. We therefore introduce a deep neural network-based generative model to enable high-fidelity, fast, electromagnetic calorimeter simulation. There are still challenges for achieving precision across the entire phase space, but our current solution can reproduce a variety of particle shower properties while achieving speedup factors of up to 100 000×. This opens the door to a new era of fast simulation that could save significant computing time and disk space, while extending the reach of physics searches and precision measurements at the LHC and beyond.
- Authors:
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- OSTI Identifier:
- 1418184
- Alternate Identifier(s):
- OSTI ID: 1485074
- Grant/Contract Number:
- AC02-05CH11231; FG02-92ER40704
- Resource Type:
- Published Article
- Journal Name:
- Physical Review Letters
- Additional Journal Information:
- Journal Name: Physical Review Letters Journal Volume: 120 Journal Issue: 4; Journal ID: ISSN 0031-9007
- Publisher:
- American Physical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Citation Formats
Paganini, Michela, de Oliveira, Luke, and Nachman, Benjamin. Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters. United States: N. p., 2018.
Web. doi:10.1103/PhysRevLett.120.042003.
Paganini, Michela, de Oliveira, Luke, & Nachman, Benjamin. Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters. United States. https://doi.org/10.1103/PhysRevLett.120.042003
Paganini, Michela, de Oliveira, Luke, and Nachman, Benjamin. Fri .
"Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters". United States. https://doi.org/10.1103/PhysRevLett.120.042003.
@article{osti_1418184,
title = {Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters},
author = {Paganini, Michela and de Oliveira, Luke and Nachman, Benjamin},
abstractNote = {Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theoretical modeling assumptions. Petabytes of simulated data are needed to develop analysis techniques, though they are expensive to generate using existing algorithms and computing resources. The modeling of detectors and the precise description of particle cascades as they interact with the material in the calorimeter are the most computationally demanding steps in the simulation pipeline. We therefore introduce a deep neural network-based generative model to enable high-fidelity, fast, electromagnetic calorimeter simulation. There are still challenges for achieving precision across the entire phase space, but our current solution can reproduce a variety of particle shower properties while achieving speedup factors of up to 100 000×. This opens the door to a new era of fast simulation that could save significant computing time and disk space, while extending the reach of physics searches and precision measurements at the LHC and beyond.},
doi = {10.1103/PhysRevLett.120.042003},
journal = {Physical Review Letters},
number = 4,
volume = 120,
place = {United States},
year = {2018},
month = {1}
}
https://doi.org/10.1103/PhysRevLett.120.042003
Web of Science
Figures / Tables:

Works referenced in this record:
Fast simulation of electromagnetic showers in the ATLAS calorimeter: Frozen showers
journal, April 2009
- Barberio, E.; Boudreau, J.; Butler, B.
- Journal of Physics: Conference Series, Vol. 160
Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1
text, January 2017
- Aad, Georges; Abbott, Brad; Abdallah, Jalal
- Deutsches Elektronen-Synchrotron, DESY, Hamburg
The Fast Simulation of The CMS Experiment
journal, December 2012
- Rahmat, Rahmat; Kroeger, Rob; Giammanco, Andrea
- Journal of Physics: Conference Series, Vol. 396, Issue 6
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
journal, January 2017
- Schawinski, Kevin; Zhang, Ce; Zhang, Hantian
- Monthly Notices of the Royal Astronomical Society: Letters
Measurement of the Inelastic Proton-Proton Cross Section at with the ATLAS Detector at the LHC
journal, October 2016
- Aaboud, M.; Aad, G.; Abbott, B.
- Physical Review Letters, Vol. 117, Issue 18
Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
journal, September 2012
- Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.
- Physics Letters B, Vol. 716, Issue 1
PYTHIA 6.4 physics and manual
journal, May 2006
- Sjöstrand, Torbjörn; Mrenna, Stephen; Skands, Peter
- Journal of High Energy Physics, Vol. 2006, Issue 05
Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
journal, September 2012
- Aad, G.; Abajyan, T.; Abbott, B.
- Physics Letters B, Vol. 716, Issue 1
Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1
journal, July 2017
- Aad, G.; Abbott, B.; Abdallah, J.
- The European Physical Journal C, Vol. 77, Issue 7
Jet-images: computer vision inspired techniques for jet tagging
journal, February 2015
- Cogan, Josh; Kagan, Michael; Strauss, Emanuel
- Journal of High Energy Physics, Vol. 2015, Issue 2
Common Accounting System for Monitoring the ATLAS Distributed Computing Resources
journal, June 2014
- Karavakis, E.; Andreeva, J.; Campana, S.
- Journal of Physics: Conference Series, Vol. 513, Issue 6
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
journal, September 2017
- de Oliveira, Luke; Paganini, Michela; Nachman, Benjamin
- Computing and Software for Big Science, Vol. 1, Issue 1
The ATLAS Simulation Infrastructure
journal, September 2010
- Aad, G.; Abbott, B.; Abdallah, J.
- The European Physical Journal C, Vol. 70, Issue 3
Geant4—a simulation toolkit
journal, July 2003
- Agostinelli, S.; Allison, J.; Amako, K.
- Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 506, Issue 3
The fast simulation of electromagnetic and hadronic showers
journal, May 1990
- Grindhammer, G.; Rudowicz, M.; Peters, S.
- Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 290, Issue 2-3
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
journal, December 2016
- Kadurin, Artur; Aliper, Alexander; Kazennov, Andrey
- Oncotarget, Vol. 8, Issue 7
Works referencing / citing this record:
Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks
journal, November 2018
- Musella, Pasquale; Pandolfi, Francesco
- Computing and Software for Big Science, Vol. 2, Issue 1
Machine learning and the physical sciences
journal, December 2019
- Carleo, Giuseppe; Cirac, Ignacio; Cranmer, Kyle
- Reviews of Modern Physics, Vol. 91, Issue 4
Conditional multichannel generative adversarial networks with an application to traffic signs representation learning
journal, April 2018
- Ghorban, Farzin; Milani, Narges; Schugk, Daniel
- Progress in Artificial Intelligence, Vol. 8, Issue 1
Learning new physics from a machine
journal, January 2019
- D’Agnolo, Raffaele Tito; Wulzer, Andrea
- Physical Review D, Vol. 99, Issue 1
How to GAN LHC events
journal, January 2019
- Butter, Anja; Plehn, Tilman; Winterhalder, Ramon
- SciPost Physics, Vol. 7, Issue 6
binary junipr: An Interpretable Probabilistic Model for Discrimination
journal, October 2019
- Andreassen, Anders; Feige, Ilya; Frye, Christopher
- Physical Review Letters, Vol. 123, Issue 18
JUNIPR: a framework for unsupervised machine learning in particle physics
journal, February 2019
- Andreassen, Anders; Feige, Ilya; Frye, Christopher
- The European Physical Journal C, Vol. 79, Issue 2
Supervised Deep Learning in High Energy Phenomenology: a Mini Review
journal, August 2019
- Abdughani, Murat; Ren, Jie; Wu, Lei
- Communications in Theoretical Physics, Vol. 71, Issue 8
Machine and deep learning applications in particle physics
journal, December 2019
- Bourilkov, Dimitri
- International Journal of Modern Physics A, Vol. 34, Issue 35
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
journal, May 2019
- Kim, Byungsoo; Azevedo, Vinicius C.; Thuerey, Nils
- Computer Graphics Forum, Vol. 38, Issue 2
Lund jet images from generative and cycle-consistent adversarial networks
journal, November 2019
- Carrazza, Stefano; Dreyer, Frédéric A.
- The European Physical Journal C, Vol. 79, Issue 11
Machine learning at the energy and intensity frontiers of particle physics
journal, August 2018
- Radovic, Alexander; Williams, Mike; Rousseau, David
- Nature, Vol. 560, Issue 7716
Energy flow networks: deep sets for particle jets
journal, January 2019
- Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
- Journal of High Energy Physics, Vol. 2019, Issue 1
Precise Simulation of Electromagnetic Calorimeter Showers Using a Wasserstein Generative Adversarial Network
journal, January 2019
- Erdmann, Martin; Glombitza, Jonas; Quast, Thorben
- Computing and Software for Big Science, Vol. 3, Issue 1
Learning to classify from impure samples with high-dimensional data
journal, July 2018
- Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin
- Physical Review D, Vol. 98, Issue 1
Neural hierarchical models of ecological populations
journal, April 2020
- Joseph, Maxwell B.
- Ecology Letters, Vol. 23, Issue 4
Energy flow polynomials: a complete linear basis for jet substructure
journal, April 2018
- Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
- Journal of High Energy Physics, Vol. 2018, Issue 4
Metric Space of Collider Events
journal, July 2019
- Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
- Physical Review Letters, Vol. 123, Issue 4
Machine learning at the energy and intensity frontiers of particle physics
journal, August 2018
- Radovic, Alexander; Williams, Mike; Rousseau, David
- Nature, Vol. 560, Issue 7716
Regressive and generative neural networks for scalar field theory
journal, July 2019
- Zhou, Kai; Endrődi, Gergely; Pang, Long-Gang
- Physical Review D, Vol. 100, Issue 1
Machine learning templates for QCD factorization in the search for physics beyond the standard model
journal, May 2019
- Lin, Joshua; Bhimji, Wahid; Nachman, Benjamin
- Journal of High Energy Physics, Vol. 2019, Issue 5
Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks
journal, July 2018
- Erdmann, Martin; Geiger, Lukas; Glombitza, Jonas
- Computing and Software for Big Science, Vol. 2, Issue 1