Built on

H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys. Rev. D 101
Original
1902
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A. Butter et al. , The Machine Learning Landscape of Top Taggers, SciPost Phys. 7
Original
1902
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J. H. Collins, K. Howe, and B. Nachman, Extending the search for new resonances with machine learning, Phys. Rev. D99
Original
1902
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J. Alsing and B. Wandelt, Nuisance hardened data compression for fast likelihood-free inference, Mon. Not. Roy. Astron. Soc. 488
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1903
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J. Lin, W. Bhimji, and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model, JHEP 05
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1903
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R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC 10.1007/JHEP08(2019)110 (2019), arXiv:1903.02433 [hep-ex]
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1903
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N. Nottbeck, C. Schmitt, and V. Büscher, Implementation of high-performance, sub-microsecond deep neural networks on FPGAs for trigger applications, JINST 14
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1903
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L. Dominé and K. Terao, Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data, Phys. Rev. D 102
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1903
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P. Holl, L. Hauertmann, B. Majorovits, O. Schulz, M. Schuster, and A. J. Zsigmond, Deep learning based pulse shape discrimination for germanium detectors, Eur. Phys. J. C 79
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S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn, and J. Thompson, Deep-Learning Jets with Uncertainties and More, SciPost Phys. 8
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B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, Uncovering latent jet substructure, Phys. Rev. D100
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A. Blance, M. Spannowsky, and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches, JHEP 10
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G. Anton et al. (EXO-200), Search for Neutrinoless Double- β \beta Decay with the Complete EXO-200 Dataset, Phys. Rev. Lett. 123
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E. Aprile et al. (XENON), XENON1T Dark Matter Data Analysis: Signal Reconstruction, Calibration and Event Selection, Phys. Rev. D 100
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J. Brehmer, F. Kling, I. Espejo, and K. Cranmer, MadMiner: Machine learning-based inference for particle physics, Comput. Softw. Big Sci. 4
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1907
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A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning, Phys. Rev. D 101
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1907
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S. Wunsch, S. Jórger, R. Wolf, and G. Quast, Reducing the dependence of the neural network function to systematic uncertainties in the input space 10.1007/s41781-020-00037-9 (2019), arXiv:1907.11674 [physics.data-an]
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1907
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G. Aad et al. (ATLAS), ATLAS b-jet identification performance and efficiency measurement with t t ¯ t{\bar{t}} events in pp collisions at s = 13 \sqrt{s}=13 TeV, Eur. Phys. J. C 79
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1907
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Z. Li et al. (nEXO), Simulation of charge readout with segmented tiles in nEXO, JINST 14
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1907
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1908
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N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, A survey on bias and fairness in machine learning (2019), arXiv:1908.09635 [cs.LG]
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1908
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A. M. Sirunyan et al. (CMS), Search for dark matter particles produced in association with a Higgs boson in proton-proton collisions at s \sqrt{\mathrm{s}} = 13 TeV, JHEP 03
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1908
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B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty 10.21468/SciPostPhys.8.6.090 (2019), arXiv:1909.03081 [hep-ph]
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1909
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S. Cheong, A. Cukierman, B. Nachman, M. Safdari, and A. Schwartzman, Parametrizing the Detector Response with Neural Networks, JINST 15
Original
1910
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T. C. Yu, Template-free Pulse Height Estimation of Microcalorimeter Responses with PCA, arXiV (2019), arXiv:1910.14261 [physics.data-an]
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1910
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CMS Collaboration, A deep neural network to search for new long-lived particles decaying to jets, Machine Learning: Science and Technology 10.1088/2632-2153/ab9023 (2020b), 1912.12238
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1912
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R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer, and M. Zanetti, Learning Multivariate New Physics 10.1140/epjc/s10052-021-08853-y (2019), arXiv:1912.12155 [hep-ph]
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1912
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R. Aaij et al. , Allen: A high level trigger on GPUs for LHCb, Comput. Softw. Big Sci. 4
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1912
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A. M. Sirunyan et al. (CMS), A deep neural network to search for new long-lived particles decaying to jets, Mach. Learn. Sci. Tech. 1
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1912
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M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects, SciPost Phys. 8
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1912
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B. Abbott et al. (D0), Search for new physics in e μ X e\mu X data at DØ using Sherlock: A quasi model independent search strategy for new physics, Phys. Rev. D62
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C. Abel et al. (nEDM), Measurement of the permanent electric dipole moment of the neutron, Phys. Rev. Lett. 124
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C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows, Phys. Rev. D 101
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C. Gao, J. Isaacson, and C. Krause, i-flow: High-dimensional Integration and Sampling with Normalizing Flows, Mach. Learn. Sci. Tech. 1
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2001
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E. Bothmann, T. Janßen, M. Knobbe, T. Schmale, and S. Schumann, Exploring phase space with Neural Importance Sampling, SciPost Phys. 8
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2001
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V. M. Abazov et al. (D0), A Quasi model independent search for new physics at large transverse momentum, Phys. Rev. D64
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B. Abbott et al. (D0), A quasi-model-independent search for new high p T p_{T} physics at DØ, Phys. Rev. Lett. 86
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G. Kasieczka and D. Shih, DisCo Fever: Robust Networks Through Distance Correlation 10.1103/PhysRevLett.125.122001 (2020), arXiv:2001.05310 [hep-ph]
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2001
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A. Andreassen, B. Nachman, and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection, Phys. Rev. D 101
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Q. Ahmad et al. (SNO), Direct evidence for neutrino flavor transformation from neutral current interactions in the Sudbury Neutrino Observatory, Phys. Rev. Lett. 89
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H. Serviansky, N. Segol, J. Shlomi, K. Cranmer, E. Gross, H. Maron, and Y. Lipman, Set2graph: Learning graphs from sets (2020), arXiv:2002.08772 [cs.LG]
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O. Amram and C. M. Suarez, Tag N’ Train: A Technique to Train Improved Classifiers on Unlabeled Data 10.1007/JHEP01(2021)153 (2020), arXiv:2002.12376 [hep-ph]
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2002
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S. Summers et al. , Fast inference of Boosted Decision Trees in FPGAs for particle physics, JINST 15
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2002
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B. Abi et al. (DUNE), Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume I Introduction to DUNE, JINST 15
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B. Abi et al. (DUNE), Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume III: DUNE Far Detector Technical Coordination, JINST 15
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S. Wunsch, S. Jörger, R. Wolf, and G. Quast, Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters, Comput. Softw. Big Sci. 5
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2003
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G. Kasieczka, M. Luchmann, F. Otterpohl, and T. Plehn, Per-Object Systematics using Deep-Learned Calibration 10.21468/SciPostPhys.9.6.089 (2020a), arXiv:2003.11099 [hep-ph]
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V. Bertacchi et al. (Belle II Tracking Group), Track finding at Belle II, Comput. Phys. Commun. 259
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A. M. Sirunyan et al. (CMS), Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques, JINST 15
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G. Aad et al. (ATLAS), Search for Higgs Boson Decays into a Z Z Boson and a Light Hadronically Decaying Resonance Using 13 TeV p p pp Collision Data from the ATLAS Detector, Phys. Rev. Lett. 125
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V. A. Kostelecky and M. Mewes, Lorentz and CPT violation in neutrinos, Phys. Rev. D 69
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S. Aiello et al. (KM3NeT), Event reconstruction for KM3NeT/ORCA using convolutional neural networks, JINST 15
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K. S. Cranmer, Searching for new physics: Contributions to LEP and the LHC , Ph.D. thesis , Wisconsin U., Madison (2005)
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J. M. Clavijo, P. Glaysher, and J. M. Katzy, Adversarial domain adaptation to reduce sample bias of a high energy physics classifier, (2020), arXiv:2005.00568 [stat.ML]
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2005
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O. Knapp, G. Dissertori, O. Cerri, T. Q. Nguyen, J.-R. Vlimant, and M. Pierini, Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark 10.1140/epjp/s13360-021-01109-4 (2020), arXiv:2005.01598 [hep-ex]
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2005
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ATLAS Collaboration, Dijet resonance search with weak supervision using 13 TeV pp collisions in the ATLAS detector 10.1103/PhysRevLett.125.131801 (2020), arXiv:2005.02983 [hep-ex]
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G. Adhikari et al. (COSINE-100), Lowering the energy threshold in COSINE-100 dark matter searches, Astropart. Phys. 130
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2007
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2007
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V. Chekalina, E. Orlova, F. Ratnikov, D. Ulyanov, A. Ustyuzhanin, and E. Zakharov, Generative Models for Fast Calorimeter Simulation.LHCb case, CHEP 2018 10.1051/epjconf/201921402034 (2018), arXiv:1812.01319 [physics.data-an]
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2018
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M. Aaboud et al. (ATLAS), Search for pair production of higgsinos in final states with at least three b b -tagged jets in s = 13 \sqrt{s}=13 TeV p p pp collisions using the ATLAS detector, Phys. Rev. D 98
Original
2018
Later among the works it cites.
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2021

Machine Learning in the Search for New Fundamental Physics

Karagiorgi, Georgia, Kasieczka, Gregor, Kravitz, Scott et al.

Understand

Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics.

We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments.
While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research.
These modern machine learning developments are the focus of the present review.

Built on

H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys. Rev. D 101
Original
1902
Earlier work this paper cites.
A. Butter et al. , The Machine Learning Landscape of Top Taggers, SciPost Phys. 7
Original
1902
Earlier work this paper cites.
J. H. Collins, K. Howe, and B. Nachman, Extending the search for new resonances with machine learning, Phys. Rev. D99
Original
1902
Earlier work this paper cites.
J. Alsing and B. Wandelt, Nuisance hardened data compression for fast likelihood-free inference, Mon. Not. Roy. Astron. Soc. 488
Original
1903
Earlier work this paper cites.
J. Lin, W. Bhimji, and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model, JHEP 05
Original
1903
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC 10.1007/JHEP08(2019)110 (2019), arXiv:1903.02433 [hep-ex]
Original
1903
Earlier work this paper cites.
N. Nottbeck, C. Schmitt, and V. Büscher, Implementation of high-performance, sub-microsecond deep neural networks on FPGAs for trigger applications, JINST 14
Original
1903
Earlier work this paper cites.
L. Dominé and K. Terao, Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data, Phys. Rev. D 102
Original
1903
Earlier work this paper cites.
P. Holl, L. Hauertmann, B. Majorovits, O. Schulz, M. Schuster, and A. J. Zsigmond, Deep learning based pulse shape discrimination for germanium detectors, Eur. Phys. J. C 79
Original
1903
Earlier work this paper cites.
M. S. Albergo, G. Kanwar, and P. E. Shanahan, Flow-based generative models for Markov chain Monte Carlo in lattice field theory, Phys. Rev. D 100
Original
1904
Earlier work this paper cites.
S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn, and J. Thompson, Deep-Learning Jets with Uncertainties and More, SciPost Phys. 8
Original
1904
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, Uncovering latent jet substructure, Phys. Rev. D100
Original
1904
Earlier work this paper cites.
A. Blance, M. Spannowsky, and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches, JHEP 10
Original
1905
Earlier work this paper cites.
G. Anton et al. (EXO-200), Search for Neutrinoless Double- β \beta Decay with the Complete EXO-200 Dataset, Phys. Rev. Lett. 123
Original
1906
Earlier work this paper cites.
E. Aprile et al. (XENON), XENON1T Dark Matter Data Analysis: Signal Reconstruction, Calibration and Event Selection, Phys. Rev. D 100
Original
1906
Earlier work this paper cites.
J. Brehmer, F. Kling, I. Espejo, and K. Cranmer, MadMiner: Machine learning-based inference for particle physics, Comput. Softw. Big Sci. 4
Original
1907
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning, Phys. Rev. D 101
Original
1907
Earlier work this paper cites.
S. Wunsch, S. Jórger, R. Wolf, and G. Quast, Reducing the dependence of the neural network function to systematic uncertainties in the input space 10.1007/s41781-020-00037-9 (2019), arXiv:1907.11674 [physics.data-an]
Original
1907
Earlier work this paper cites.
G. Aad et al. (ATLAS), ATLAS b-jet identification performance and efficiency measurement with t t ¯ t{\bar{t}} events in pp collisions at s = 13 \sqrt{s}=13 TeV, Eur. Phys. J. C 79
Original
1907
Earlier work this paper cites.
Z. Li et al. (nEXO), Simulation of charge readout with segmented tiles in nEXO, JINST 14
Original
1907
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, Mass Agnostic Jet Taggers 10.21468/SciPostPhys.8.1.011 (2019), arXiv:1908.08959 [hep-ph]
Original
1908
Earlier work this paper cites.
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, A survey on bias and fairness in machine learning (2019), arXiv:1908.09635 [cs.LG]
Original
1908
Earlier work this paper cites.
A. M. Sirunyan et al. (CMS), Search for dark matter particles produced in association with a Higgs boson in proton-proton collisions at s \sqrt{\mathrm{s}} = 13 TeV, JHEP 03
Original
1908
Earlier work this paper cites.
B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty 10.21468/SciPostPhys.8.6.090 (2019), arXiv:1909.03081 [hep-ph]
Original
1909
Earlier work this paper cites.
S. Cheong, A. Cukierman, B. Nachman, M. Safdari, and A. Schwartzman, Parametrizing the Detector Response with Neural Networks, JINST 15
Original
1910
Earlier work this paper cites.
T. C. Yu, Template-free Pulse Height Estimation of Microcalorimeter Responses with PCA, arXiV (2019), arXiv:1910.14261 [physics.data-an]
Original
1910
Earlier work this paper cites.
CMS Collaboration, A deep neural network to search for new long-lived particles decaying to jets, Machine Learning: Science and Technology 10.1088/2632-2153/ab9023 (2020b), 1912.12238
Original
1912
Earlier work this paper cites.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer, and M. Zanetti, Learning Multivariate New Physics 10.1140/epjc/s10052-021-08853-y (2019), arXiv:1912.12155 [hep-ph]
Original
1912
Earlier work this paper cites.
R. Aaij et al. , Allen: A high level trigger on GPUs for LHCb, Comput. Softw. Big Sci. 4
Original
1912
Earlier work this paper cites.
A. M. Sirunyan et al. (CMS), A deep neural network to search for new long-lived particles decaying to jets, Mach. Learn. Sci. Tech. 1
Original
1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects, SciPost Phys. 8
Original
1912
Earlier work this paper cites.
J. Neyman and E. S. Pearson, On the problem of the most efficient tests of statistical hypotheses, Phil. Trans. R. Soc. Lond. A 231
1933
Earlier work this paper cites.
K. Fukushima and S. Miyake, Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition, in Competition and cooperation in neural nets (Springer, 1982) pp. 267–285
1982
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, nature 323
1986
Earlier work this paper cites.
Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel, Handwritten digit recognition with a back-propagation network, Advances in neural information processing systems 2
1989
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural computation 9
1997
Earlier work this paper cites.
Y. Fukuda et al. (Super-Kamiokande), Evidence for oscillation of atmospheric neutrinos, Phys. Rev. Lett. 81
1998
Earlier work this paper cites.
B. Knuteson, A Quasi-Model-Independent Search for New High p T p_{T} Physics at D0 , ph.D. thesis, University of California at Berkeley (2000)
2000
Earlier work this paper cites.
B. Abbott et al. (D0), Search for new physics in e μ X e\mu X data at DØ using Sherlock: A quasi model independent search strategy for new physics, Phys. Rev. D62
2000
Earlier work this paper cites.
C. Abel et al. (nEDM), Measurement of the permanent electric dipole moment of the neutron, Phys. Rev. Lett. 124
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows, Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson, and C. Krause, i-flow: High-dimensional Integration and Sampling with Normalizing Flows, Mach. Learn. Sci. Tech. 1
Original
2001
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale, and S. Schumann, Exploring phase space with Neural Importance Sampling, SciPost Phys. 8
Original
2001
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation, Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
V. M. Abazov et al. (D0), A Quasi model independent search for new physics at large transverse momentum, Phys. Rev. D64
2001
Earlier work this paper cites.
B. Abbott et al. (D0), A quasi-model-independent search for new high p T p_{T} physics at DØ, Phys. Rev. Lett. 86
2001
Earlier work this paper cites.
G. Kasieczka and D. Shih, DisCo Fever: Robust Networks Through Distance Correlation 10.1103/PhysRevLett.125.122001 (2020), arXiv:2001.05310 [hep-ph]
Original
2001
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection, Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
Q. Ahmad et al. (SNO), Direct evidence for neutrino flavor transformation from neutral current interactions in the Sudbury Neutrino Observatory, Phys. Rev. Lett. 89
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