Machine learning at the energy and intensity frontiers of particle physics
Abstract
Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.
- Authors:
-
- College of William and Mary, Williamsburg, VA (United States)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Univ. Paris-Sud, Orsay (France)
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Univ. di Bologna, Bologna (Italy); INFN Sezione di Bologna, Bologna (Italy)
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Univ. of Cincinnati, Cincinnati, OH (United States)
- Tufts Univ., Medford, MA (United States)
- Publication Date:
- Research Org.:
- SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- OSTI Identifier:
- 1469751
- Alternate Identifier(s):
- OSTI ID: 1498560
- Report Number(s):
- FERMILAB-PUB-18-436-ND
Journal ID: ISSN 0028-0836; PII: 361
- Grant/Contract Number:
- AC02-76SF00515; AC02-07CH11359
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Nature (London)
- Additional Journal Information:
- Journal Volume: 560; Journal Issue: 7716; Journal ID: ISSN 0028-0836
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Citation Formats
Radovic, Alexander, Williams, Mike, Rousseau, David, Kagan, Michael, Bonacorsi, Daniele, Himmel, Alexander, Aurisano, Adam, Terao, Kazuhiro, and Wongjirad, Taritree. Machine learning at the energy and intensity frontiers of particle physics. United States: N. p., 2018.
Web. doi:10.1038/s41586-018-0361-2.
Radovic, Alexander, Williams, Mike, Rousseau, David, Kagan, Michael, Bonacorsi, Daniele, Himmel, Alexander, Aurisano, Adam, Terao, Kazuhiro, & Wongjirad, Taritree. Machine learning at the energy and intensity frontiers of particle physics. United States. https://doi.org/10.1038/s41586-018-0361-2
Radovic, Alexander, Williams, Mike, Rousseau, David, Kagan, Michael, Bonacorsi, Daniele, Himmel, Alexander, Aurisano, Adam, Terao, Kazuhiro, and Wongjirad, Taritree. Wed .
"Machine learning at the energy and intensity frontiers of particle physics". United States. https://doi.org/10.1038/s41586-018-0361-2. https://www.osti.gov/servlets/purl/1469751.
@article{osti_1469751,
title = {Machine learning at the energy and intensity frontiers of particle physics},
author = {Radovic, Alexander and Williams, Mike and Rousseau, David and Kagan, Michael and Bonacorsi, Daniele and Himmel, Alexander and Aurisano, Adam and Terao, Kazuhiro and Wongjirad, Taritree},
abstractNote = {Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.},
doi = {10.1038/s41586-018-0361-2},
url = {https://www.osti.gov/biblio/1469751},
journal = {Nature (London)},
issn = {0028-0836},
number = 7716,
volume = 560,
place = {United States},
year = {2018},
month = {8}
}
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Works referencing / citing this record:
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
journal, August 1997
- Freund, Yoav; Schapire, Robert E.
- Journal of Computer and System Sciences, Vol. 55, Issue 1
Measurement of the tau polarisation at the Z resonance
journal, September 1993
- Buskulic, D.; Decamp, D.; Goy, C.
- Zeitschrift f�r Physik C Particles and Fields, Vol. 59, Issue 3
A measurement of the production of D*± mesons on the Z0 resonance
journal, March 1995
- Akers, R.; Alexander, G.; Allison, J.
- Zeitschrift für Physik C Particles and Fields, Vol. 67, Issue 1
Determination of ${\rm |V_{ub}|}$ from the measurement of the inclusive charmless semileptonic branching ratio of b hadrons
journal, January 1999
- Barate et al., R.
- The European Physical Journal C, Vol. 6, Issue 4
ImageNet Large Scale Visual Recognition Challenge
journal, April 2015
- Russakovsky, Olga; Deng, Jia; Su, Hao
- International Journal of Computer Vision, Vol. 115, Issue 3
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
Neural networks and cellular automata in experimental high energy physics
journal, June 1988
- Denby, B.
- Computer Physics Communications, Vol. 49, Issue 3
Pattern recognition in high energy physics with artificial neural networks — JETNET 2.0
journal, May 1992
- Lönnblad, Leif; Peterson, Carsten; Rögnvalsson, Thorsteinn
- Computer Physics Communications, Vol. 70, Issue 1
JETNET 3.0—A versatile artificial neural network package
journal, June 1994
- Peterson, Carsten; Rögnvaldsson, Thorsteinn; Lönnblad, Leif
- Computer Physics Communications, Vol. 81, Issue 1-2
The use of neural networks in γ-π0 discrimination
journal, June 1993
- Babbage, Wayne S.; Thompson, Lee F.
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Higgs search by neural networks at LHC
journal, February 1994
- Chiappetta, P.; Colangelo, P.; De Felice, P.
- Physics Letters B, Vol. 322, Issue 3
Using neural networks to identify jets
journal, February 1991
- Lönnblad, Leif; Peterson, Carsten; Rögnvaldsson, Thorsteinn
- Nuclear Physics B, Vol. 349, Issue 3
Boosted decision trees as an alternative to artificial neural networks for particle identification
journal, May 2005
- Roe, Byron P.; Yang, Hai-Jun; Zhu, Ji
- Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 543, Issue 2-3
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
journal, September 2017
- Wielgosz, Maciej; Skoczeń, Andrzej; Mertik, Matej
- Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 867
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
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
Learning representations by back-propagating errors
journal, October 1986
- Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J.
- Nature, Vol. 323, Issue 6088
Deep learning
journal, May 2015
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
- Nature, Vol. 521, Issue 7553
Mastering the game of Go with deep neural networks and tree search
journal, January 2016
- Silver, David; Huang, Aja; Maddison, Chris J.
- Nature, Vol. 529, Issue 7587
Searching for exotic particles in high-energy physics with deep learning
journal, July 2014
- Baldi, P.; Sadowski, P.; Whiteson, D.
- Nature Communications, Vol. 5, Issue 1
Monitoring data transfer latency in CMS computing operations
journal, December 2015
- Bonacorsi, D.; Diotalevi, T.; Magini, N.
- Journal of Physics: Conference Series, Vol. 664, Issue 3
Disk storage management for LHCb based on Data Popularity estimator
journal, December 2015
- Hushchyn, Mikhail; Charpentier, Philippe; Ustyuzhanin, Andrey
- Journal of Physics: Conference Series, Vol. 664, Issue 4
LHCb Topological Trigger Reoptimization
journal, December 2015
- Likhomanenko, Tatiana; Ilten, Philip; Khairullin, Egor
- Journal of Physics: Conference Series, Vol. 664, Issue 8
Predicting dataset popularity for the CMS experiment
journal, October 2016
- Kuznetsov, V.; Li, T.; Giommi, L.
- Journal of Physics: Conference Series, Vol. 762
GRID Storage Optimization in Transparent and User-Friendly Way for LHCb Datasets
journal, October 2017
- Hushchyn, M.; Ustyuzhanin, A.; Charpentier, P.
- Journal of Physics: Conference Series, Vol. 898
LHCb trigger streams optimization
journal, October 2017
- Derkach, D.; Kazeev, N.; Neychev, R.
- Journal of Physics: Conference Series, Vol. 898
Towards automation of data quality system for CERN CMS experiment
journal, October 2017
- Borisyak, M.; Ratnikov, F.; Derkach, D.
- Journal of Physics: Conference Series, Vol. 898
New approaches for boosting to uniformity
journal, March 2015
- Rogozhnikov, A.; Bukva, A.; Gligorov, V.
- Journal of Instrumentation, Vol. 10, Issue 03
Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at √ s = 8 TeV
journal, June 2015
- ,
- Journal of Instrumentation, Vol. 10, Issue 06, p. P06005-P06005
Background rejection in NEXT using deep neural networks
journal, January 2017
- Renner, J.; Farbin, A.; Vidal, J. Muñoz
- Journal of Instrumentation, Vol. 12, Issue 01
Design and construction of the MicroBooNE detector
journal, February 2017
- Acciarri, R.; Adams, C.; An, R.
- Journal of Instrumentation, Vol. 12, Issue 02
Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
journal, March 2017
- Acciarri, R.; Adams, C.; An, R.
- Journal of Instrumentation, Vol. 12, Issue 03
LHC Machine
journal, August 2008
- Evans, Lyndon; Bryant, Philip
- Journal of Instrumentation, Vol. 3, Issue 08
Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree
journal, February 2013
- Gligorov, V. V.; Williams, M.
- Journal of Instrumentation, Vol. 8, Issue 02
The LHCb trigger and its performance in 2011
journal, April 2013
- Aaij, R.; Albrecht, J.; Alessio, F.
- Journal of Instrumentation, Vol. 8, Issue 04
uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers
journal, December 2013
- Stevens, J.; Williams, M.
- Journal of Instrumentation, Vol. 8, Issue 12
Efficient antihydrogen detection in antimatter physics by deep learning
journal, September 2017
- Sadowski, P.; Radics, B.
- Journal of Physics Communications, Vol. 1, Issue 2
Some Effects of Ionizing Radiation on the Formation of Bubbles in Liquids
journal, August 1952
- Glaser, Donald A.
- Physical Review, Vol. 87, Issue 4
Search for the standard model Higgs boson produced in association with a or a boson and decaying to bottom quarks
journal, January 2014
- Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.
- Physical Review D, Vol. 89, Issue 1
Jet substructure classification in high-energy physics with deep neural networks
journal, May 2016
- Baldi, Pierre; Bauer, Kevin; Eng, Clara
- Physical Review D, Vol. 93, Issue 9
Jet flavor classification in high-energy physics with deep neural networks
journal, December 2016
- Guest, Daniel; Collado, Julian; Baldi, Pierre
- Physical Review D, Vol. 94, Issue 11
Parton shower uncertainties in jet substructure analyses with deep neural networks
journal, January 2017
- Barnard, James; Dawe, Edmund Noel; Dolan, Matthew J.
- Physical Review D, Vol. 95, Issue 1
Search for active-sterile neutrino mixing using neutral-current interactions in NOvA
journal, October 2017
- Adamson, P.; Aliaga, L.; Ambrose, D.
- Physical Review D, Vol. 96, Issue 7
Decorrelated jet substructure tagging using adversarial neural networks
journal, October 2017
- Shimmin, Chase; Sadowski, Peter; Baldi, Pierre
- Physical Review D, Vol. 96, Issue 7
Search for Hidden-Sector Bosons in Decays
journal, October 2015
- Aaij, R.; Adeva, B.; Adinolfi, M.
- Physical Review Letters, Vol. 115, Issue 16
Measurement of the Branching Fraction and Effective Lifetime and Search for Decays
journal, May 2017
- Aaij, R.; Adeva, B.; Adinolfi, M.
- Physical Review Letters, Vol. 118, Issue 19
Constraints on Oscillation Parameters from Appearance and Disappearance in NOvA
journal, June 2017
- Adamson, P.; Aliaga, L.; Ambrose, D.
- Physical Review Letters, Vol. 118, Issue 23
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters
journal, January 2018
- Paganini, Michela; de Oliveira, Luke; Nachman, Benjamin
- Physical Review Letters, Vol. 120, Issue 4
Search for Dark Photons Produced in 13 TeV Collisions
journal, February 2018
- Aaij, R.; Adeva, B.; Adinolfi, M.
- Physical Review Letters, Vol. 120, Issue 6
Finding gluon jets with a neural trigger
journal, September 1990
- Lönnblad, Leif; Peterson, Carsten; Rögnvaldsson, Thorsteinn
- Physical Review Letters, Vol. 65, Issue 11
Going deeper with convolutions
conference, June 2015
- Szegedy, Christian
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks
conference, December 2016
- Racah, Evan; Ko, Seyoon; Sadowski, Peter
- 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
Neural Networks for Modeling and Control of Particle Accelerators
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