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  • Review Article
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Machine learning in the search for new fundamental physics

Nature Reviews Physics volume 4, pages 399–412 (2022)Cite this article

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Abstract

Compelling experimental evidence suggests the existence of new physics beyond the well-established and tested standard model of particle physics. Various current and upcoming experiments are searching for signatures of new physics. Despite the variety of approaches and theoretical models tested in these experiments, what they all have in common is the very large volume of complex data that they produce. This data challenge calls for powerful statistical methods. Machine learning has been in use in high-energy particle physics for well over a decade, but the rise of deep learning in the 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, which discusses 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.

Key points

  • There have been large and sustained developments of deep learning in high-energy physics over the past several years.

  • Supervised machine learning methods are widely used to identify known particles and to design targeted searches for specific theories of new physics.

  • Less-than-supervised machine learning methods are used to carry out searches that depend less on a specific signal model.

  • Experiments such as those at the Large Hadron Collider, neutrino detectors and rare event searches for dark matter, despite having different technical requirements, also share similarities, and there is ground for cooperation to develop machine learning methods.

  • Combining physics and new ideas from statistical learning will be crucial to analysing the large volumes of data to potentially uncover the fundamental structure of nature.

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Fig. 1: Illustration of the landscape of model dependence.

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References

  1. Aad, G. et al. Observation of a new particle in the search for the standard model Higgs boson with the ATLAS detector at the LHC. Phys. Lett. B 716, 1–29 (2012).

    Article  ADS  Google Scholar 

  2. Chatrchyan, S. et al. Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC. Phys. Lett. B 716, 30–61 (2012).

    Article  ADS  Google Scholar 

  3. Zyla, P. et al. Review of particle physics. Prog. Theor. Exp. Phys. 2020, 083C01 (2020).

    Article  Google Scholar 

  4. Fukuda, Y. et al. Evidence for oscillation of atmospheric neutrinos. Phys. Rev. Lett. 81, 1562–1567 (1998).

    Article  ADS  Google Scholar 

  5. Ahmad, Q. et al. Direct evidence for neutrino flavor transformation from neutral current interactions in the Sudbury Neutrino Observatory. Phys. Rev. Lett. 89, 011301 (2002).

    Article  ADS  Google Scholar 

  6. Canetti, L., Drewes, M. & Shaposhnikov, M. Matter and antimatter in the Universe. New J. Phys. 14, 095012 (2012).

    Article  ADS  MATH  Google Scholar 

  7. Abel, C. et al. Measurement of the permanent electric dipole moment of the neutron. Phys. Rev. Lett. 124, 081803 (2020).

    Article  ADS  Google Scholar 

  8. Hocker, A. et al. TMVA — toolkit for multivariate data analysis. Preprint at arXiv https://arxiv.org/abs/physics/0703039 (2007).

  9. Deiana, A. M. et al. Applications and techniques for fast machine learning in science. Preprint at arXiv https://arxiv.org/abs/2110.13041 (2021).

  10. Radovic, A. et al. Machine learning at the energy and intensity frontiers of particle physics. Nature 560, 41–48 (2018).

    Article  ADS  Google Scholar 

  11. Feickert, M. & Nachman, B. A living review of machine learning for particle physics. Preprint at arXiv https://arxiv.org/abs/2102.02770 (2021).

  12. Bellagente, M., Butter, A., Kasieczka, G., Plehn, T. & Winterhalder, R. How to GAN away detector effects. SciPost Phys. 8, 070 (2020).

    Article  ADS  Google Scholar 

  13. Komiske, P., McCormack, W. P. & Nachman, B. Preserving new physics while simultaneously unfolding all observables. Preprint at arXiv https://arxiv.org/abs/2105.09923 (2021).

  14. Brehmer, J., Kling, F., Espejo, I. & Cranmer, K. MadMiner: machine learning-based inference for particle physics. Comput. Softw. Big Sci. 4, 3 (2020).

    Article  Google Scholar 

  15. Brehmer, J., Louppe, G., Pavez, J. & Cranmer, K. Mining gold from implicit models to improve likelihood-free inference. Proc. Natl Acad. Sci. USA 117, 5242–5249 (2020).

  16. Brehmer, J., Cranmer, K., Louppe, G. & Pavez, J. Constraining effective field theories with machine learning. Phys. Rev. Lett. 121, 111801 (2018).

    Article  ADS  Google Scholar 

  17. Brehmer, J., Cranmer, K., Louppe, G. & Pavez, J. A guide to constraining effective field theories with machine learning. Phys. Rev. D 98, 052004 (2018).

    Article  ADS  Google Scholar 

  18. Grojean, C., Paul, A. & Qian, Z. Resurrecting \(b\bar{b}h\) with kinematic shapes. Preprint at arXiv https://arxiv.org/abs/2011.13945 (2020).

  19. Chatterjee, S., Frohner, N., Lechner, L., Schöfbeck, R. & Schwarz, D. Tree boosting for learning EFT parameters. Preprint at arXiv https://arxiv.org/abs/2107.10859 (2021).

  20. Chen, S., Glioti, A., Panico, G. & Wulzer, A. Parametrized classifiers for optimal EFT sensitivity. J. High Energy Phys. 05, 247 (2021).

    Article  ADS  MATH  Google Scholar 

  21. Erbin, H. & Krippendorf, S. GANs for generating EFT models. Phys. Lett. B 810, 135798 (2020).

    Article  MathSciNet  MATH  Google Scholar 

  22. Caron, S., Kim, J. S., Rolbiecki, K., Ruiz de Austri, R. & Stienen, B. The BSM-AI project: SUSY-AI – generalizing LHC limits on supersymmetry with machine learning. Eur. Phys. J. C 77, 257 (2017).

    Article  ADS  Google Scholar 

  23. Bertone, G. et al. Accelerating the BSM interpretation of LHC data with machine learning. Phys. Dark Univ. 24, 100293 (2019).

    Article  Google Scholar 

  24. Kronheim, B. S., Kuchera, M. P., Prosper, H. B. & Karbo, A. Bayesian neural networks for fast SUSY predictions. Phys. Lett. B 813, 136041 (2021).

    Article  Google Scholar 

  25. Fukushima, K. & Miyake, S. in Competition and Cooperation in Neural Nets (eds Amari, S. & Arbib, M. A.) 267–285 (Springer, 1982).

  26. LeCun, Y. et al. Handwritten digit recognition with a back-propagation network. Adv. Neural Inf. Process. Syst. 2, 396–404 (1989).

    Google Scholar 

  27. de Oliveira, L., Kagan, M., Mackey, L., Nachman, B. & Schwartzman, A. Jet-images — deep learning edition. J. High Energy Phys. 07, 069 (2016).

    Article  Google Scholar 

  28. Baldi, P., Bauer, K., Eng, C., Sadowski, P. & Whiteson, D. Jet substructure classification in high-energy physics with deep neural networks. Phys. Rev. D 93, 094034 (2016).

    Article  ADS  Google Scholar 

  29. Aurisano, A. et al. A convolutional neural network neutrino event classifier. J. Instrum. 11, P09001 (2016).

    Article  Google Scholar 

  30. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

    Article  ADS  MATH  Google Scholar 

  31. Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–80 (1997).

    Article  Google Scholar 

  32. Guest, D. et al. Jet flavor classification in high-energy physics with deep neural networks. Phys. Rev. D 94, 112002 (2016).

    Article  ADS  Google Scholar 

  33. Louppe, G., Cho, K., Becot, C. & Cranmer, K. QCD-aware recursive neural networks for jet physics. J. High Energy Phys. 01, 057 (2019).

    Article  ADS  Google Scholar 

  34. Dolan, M. J. & Ore, A. Equivariant energy flow networks for jet tagging. Phys. Rev. D 103, 074022 (2021).

    Article  ADS  Google Scholar 

  35. Serviansky, H. et al. Set2graph: learning graphs from sets. Preprint at arXiv https://arxiv.org/abs/2002.08772 (2020).

  36. Bogatskiy, A. et al. Lorentz group equivariant neural network for particle physics. Preprint at arXiv https://arxiv.org/abs/2006.04780 (2020).

  37. Shimmin, C. Particle convolution for high energy physics. Preprint at arXiv https://arxiv.org/abs/2107.02908 (2021).

  38. Zaheer, M. et al. Deep sets. Adv. Neural Inf. Process. Syst. 30, 3391–3401 (2017).

    Google Scholar 

  39. Komiske, P. T., Metodiev, E. M. & Thaler, J. Energy flow networks: deep sets for particle jets. J. High Energy Phys. 01, 121 (2019).

    Article  ADS  Google Scholar 

  40. Henrion, I. et al. Neural message passing for jet physics. in Proc. Workshop Deep Learning Physical Sciences (NIPS, 2017).

  41. Choma, N. et al. Graph neural networks for IceCube signal classification. Preprint at arXiv https://arxiv.org/abs/1809.06166 (2018).

  42. Abdughani, M., Ren, J., Wu, L. & Yang, J. M. Probing stop pair production at the LHC with graph neural networks. J. High Energy Phys. 08, 055 (2019).

    Article  ADS  Google Scholar 

  43. Arjona Martínez, J., Cerri, O., Pierini, M., Spiropulu, M. & Vlimant, J.-R. Pileup mitigation at the Large Hadron Collider with graph neural networks. Eur. Phys. J. Plus 134, 333 (2019).

    Article  Google Scholar 

  44. Qu, H. & Gouskos, L. ParticleNet: jet tagging via particle clouds. Phys. Rev. D 101, 056019 (2020).

    Article  ADS  Google Scholar 

  45. Moreno, E. A. et al. JEDI-net: a jet identification algorithm based on interaction networks. Eur. Phys. J. C 80, 58 (2020).

    Article  ADS  Google Scholar 

  46. Moreno, E. A. et al. Interaction networks for the identification of boosted \(H\to b\overline{b}\) decays. Phys. Rev. D 102, 012010 (2020).

    Article  ADS  Google Scholar 

  47. Shlomi, J., Battaglia, P. & Vlimant, J.-R. Graph neural networks in particle physics. Mach. Learn. Sci. Technol. 2, 021001 (2021).

    Article  Google Scholar 

  48. Cheong, S., Cukierman, A., Nachman, B., Safdari, M. & Schwartzman, A. Parametrizing the detector response with neural networks. J. Instrum. 15, P01030 (2020).

    Article  Google Scholar 

  49. Goodfellow, I. J. et al. in Proc. 27th Int. Conf. Neural Inform. Process. Syst. Vol. 2 (eds Ghahramani, Z. et al.) 2672–2680 (MIT Press, 2014).

  50. Creswell, A. et al. Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35, 53 (2018).

    Article  ADS  Google Scholar 

  51. Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at arXiv https://arxiv.org/abs/1312.6114 (2014).

  52. Kingma, D. P. & Welling, M. An introduction to variational autoencoders. Found. Trends Mach. Learn. 12, 307 (2019).

    Article  MATH  Google Scholar 

  53. Rezende, D. J. & Mohamed, S. Variational inference with normalizing flows. Proc. Mach. Learn. Res. 37, 1530–1538 (2015).

    Google Scholar 

  54. Kobyzev, I., Prince, S. & Brubaker, M. Normalizing flows: an introduction and review of current methods. IEEE Trans. Pattern Anal. Mach. Intel. 43, 3964–3979 (2021).

    Article  Google Scholar 

  55. de Oliveira, L., Paganini, M. & Nachman, B. Learning particle physics by example: location-aware generative adversarial networks for physics synthesis. Comput. Softw. Big Sci. 1, 4 (2017).

    Article  Google Scholar 

  56. Mustafa, M. et al. CosmoGAN: creating high-fidelity weak lensing convergence maps using generative adversarial networks. Comput. Astrophys. Cosmol. 6, 1 (2019).

    Article  ADS  Google Scholar 

  57. ATLAS collaboration. Deep generative models for fast shower simulation in ATLAS. Report ATL-SOFT-PUB-2018-001 (CERN, 2018).

  58. Hajer, J., Li, Y.-Y., Liu, T. & Wang, H. Novelty detection meets collider physics. Phys. Rev. D 101, 076015 (2020).

    Article  ADS  Google Scholar 

  59. Farina, M., Nakai, Y. & Shih, D. Searching for new physics with deep autoencoders. Phys. Rev. D 101, 075021 (2020).

    Article  ADS  Google Scholar 

  60. Heimel, T., Kasieczka, G., Plehn, T. & Thompson, J. M. QCD or what? SciPost Phys. 6, 030 (2019).

    Article  ADS  Google Scholar 

  61. Albergo, M. S., Kanwar, G. & Shanahan, P. E. Flow-based generative models for Markov chain Monte Carlo in lattice field theory. Phys. Rev. D 100, 034515 (2019).

    Article  ADS  MathSciNet  Google Scholar 

  62. Gao, C., Höche, S., Isaacson, J., Krause, C. & Schulz, H. Event generation with normalizing flows. Phys. Rev. D 101, 076002 (2020).

    Article  ADS  Google Scholar 

  63. Gao, C., Isaacson, J. & Krause, C. i-flow: high-dimensional integration and sampling with normalizing flows. Mach. Learn. Sci. Tech. 1, 045023 (2020).

    Article  Google Scholar 

  64. Bothmann, E., Janßen, T., Knobbe, M., Schmale, T. & Schumann, S. Exploring phase space with neural importance sampling. SciPost Phys. 8, 069 (2020).

    Article  ADS  Google Scholar 

  65. Nachman, B. & Shih, D. Anomaly detection with density estimation. Phys. Rev. D 101, 075042 (2020).

    Article  ADS  Google Scholar 

  66. Zhou, Z.-H. A brief introduction to weakly supervised learning. Natl Sci. Rev. 5, 44–53 (2017).

    Article  ADS  Google Scholar 

  67. Dery, L. M., Nachman, B., Rubbo, F. & Schwartzman, A. Weakly supervised classification in high energy physics. J. High Energy Phys. 05, 145 (2017).

    Article  ADS  MATH  Google Scholar 

  68. Metodiev, E. M., Nachman, B. & Thaler, J. Classification without labels: learning from mixed samples in high energy physics. J. High Energy Phys. 10, 174 (2017).

    Article  ADS  Google Scholar 

  69. Cohen, T., Freytsis, M. & Ostdiek, B. (Machine) learning to do more with less. J. High Energy Phys. 02, 034 (2018).

    Article  ADS  Google Scholar 

  70. Komiske, P. T., Metodiev, E. M., Nachman, B. & Schwartz, M. D. Learning to classify from impure samples with high-dimensional data. Phys. Rev. D 98, 011502 (2018).

    Article  ADS  Google Scholar 

  71. Knuteson, B. A Quasi-Model-Independent Search for New High pT Physics at D0. Thesis, Univ. California Berkeley (2000).

  72. Abbott, B. et al. Search for new physics in eμX data at DØ using Sherlock: a quasi model independent search strategy for new physics. Phys. Rev. D 62, 092004 (2000).

    Article  ADS  Google Scholar 

  73. Abazov, V. M. et al. A quasi model independent search for new physics at large transverse momentum. Phys. Rev. D 64, 012004 (2001).

    Article  ADS  Google Scholar 

  74. Abbott, B. et al. A quasi-model-independent search for new high pT physics at DØ. Phys. Rev. Lett. 86, 3712–3717 (2001).

    Article  ADS  Google Scholar 

  75. Aaron, F. D. et al. A general search for new phenomena at HERA. Phys. Lett. B 674, 257–268 (2009).

    Article  ADS  Google Scholar 

  76. Aktas, A. et al. A general search for new phenomena in ep scattering at HERA. Phys. Lett. B 602, 14–30 (2004).

    Article  ADS  Google Scholar 

  77. Cranmer, K. S. Searching for New Physics: Contributions to LEP and the LHC. Thesis, Wisconsin Univ. Madison (2005).

  78. Aaltonen, T. et al. (CDF Collaboration). Model-independent and quasi-model-independent search for new physics at CDF. Phys. Rev. D 78, 012002 (2008).

    Article  ADS  Google Scholar 

  79. Aaltonen, T. et al. (CDF Collaboration). Model-independent global search for new high-pT physics at CDF. Preprint at arXiv https://arxiv.org/abs/0712.2534 (2007).

  80. Aaltonen, T. et al. (CDF Collaboration). Global search for new physics with 2.0 fb−1 at CDF. Phys. Rev. D 79, 011101 (2009).

    Article  ADS  Google Scholar 

  81. CMS Collaboration. MUSiC, a model unspecific search for new physics, in pp collisions at \(\sqrt{s}=8\) TeV. Technical Report CMS-PAS-EXO-14-016 (CERN, 2017).

  82. CMS Collaboration. Model unspecific search for new physics in pp collisions at \(\sqrt{s}=7\) TeV. Technical Report CMS-PAS-EXO-10-021 (CERN, 2011).

  83. CMS Collaboration. MUSiC, a model unspecific search for new physics, in pp collisions at \(\sqrt{s}=13\) TeV. Technical Report CMS-PAS-EXO-19-008 (CERN, 2020).

  84. Sirunyan, A. M. et al. MUSiC: a model-unspecific search for new physics in proton–proton collisions at \(\sqrt{s}=13\) TeV. Eur. Phys. J. C 81, 629 (2021).

    Article  ADS  Google Scholar 

  85. Aaboud, M. et al. A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment. Eur. Phys. J. C 79, 120 (2019).

    Article  ADS  Google Scholar 

  86. A general search for new phenomena with the ATLAS detector in pp collisions at \(\sqrt{s}=8\) TeV. Report ATLAS-CONF-2014-006 (CERN, 2014).

  87. A general search for new phenomena with the ATLAS detector in pp collisions at \(\sqrt{s}=7\) TeV. Report ATLAS-CONF-2012-107 (CERN, 2012).

  88. Butter, A. et al. The machine learning landscape of top taggers. SciPost Phys. 7, 014 (2019).

    Article  ADS  Google Scholar 

  89. Abratenko, P. et al. Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber. Phys. Rev. D 103, 092003 (2021).

    Article  ADS  Google Scholar 

  90. Baldi, P., Sadowski, P. & Whiteson, D. Searching for exotic particles in high-energy physics with deep learning. Nat. Commun. 5, 4308 (2014).

    Article  ADS  Google Scholar 

  91. Bhimji, W. et al. Deep neural networks for physics analysis on low-level whole-detector data at the LHC. J. Phys. Conf. Ser. 1085, 042034 (2018).

    Article  Google Scholar 

  92. Wunsch, S., Jörger, S., Wolf, R. & Quast, G. 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, 4 (2021).

    Article  ADS  Google Scholar 

  93. Elwood, A., Krücker, D. & Shchedrolosiev, M. Direct optimization of the discovery significance in machine learning for new physics searches in particle colliders. J. Phys. Conf. Ser. 1525, 012110 (2020).

    Article  Google Scholar 

  94. Xia, L.-G. QBDT, a new boosting decision tree method with systematical uncertainties into training for high energy physics. Nucl. Instrum. Meth A 930, 15–26 (2019).

    Article  ADS  Google Scholar 

  95. De Castro, P. & Dorigo, T. INFERNO: inference-aware neural optimisation. Comput. Phys. Commun. 244, 170–179 (2019).

    Article  ADS  Google Scholar 

  96. Charnock, T., Lavaux, G. & Wandelt, B. D. Automatic physical inference with information maximizing neural networks. Phys. Rev. D 97, 083004 (2018).

    Article  ADS  Google Scholar 

  97. Alsing, J. & Wandelt, B. Nuisance hardened data compression for fast likelihood-free inference. Mon. Not. R. Astron. Soc. 488, 5093–5103 (2019).

    Article  ADS  Google Scholar 

  98. Heinrich, L. & Simpson, N. pyhf/neos: initial zenodo release. zenodo https://doi.org/10.5281/zenodo.3697981 (2020).

  99. Dorigo, T. & de Castro, P. Dealing with nuisance parameters using machine learning in high energy physics: a review. Preprint at arXiv https://arxiv.org/abs/2007.09121 (2020).

  100. Kasieczka, G., Luchmann, M., Otterpohl, F. & Plehn, T. Per-object systematics using deep-learned calibration. SciPost Phys. 9, 089 (2020).

    Article  ADS  MathSciNet  Google Scholar 

  101. Bollweg, S. et al. Deep-learning jets with uncertainties and more. SciPost Phys. 8, 006 (2020).

    Article  ADS  MathSciNet  Google Scholar 

  102. Araz, J. Y. & Spannowsky, M. Combine and conquer: event reconstruction with Bayesian ensemble neural networks. J. High Energy Phys. 04, 296 (2021).

    Article  ADS  Google Scholar 

  103. Bellagente, M., Haußmann, M., Luchmann, M. & Plehn, T. Understanding event-generation networks via uncertainties. Preprint at arXiv https://arxiv.org/abs/2104.04543 (2021).

  104. Nachman, B. A guide for deploying deep learning in LHC searches: how to achieve optimality and account for uncertainty. SciPost Phys. 8, 090 (2020).

    Article  ADS  MathSciNet  Google Scholar 

  105. Ghosh, A., Nachman, B. & Whiteson, D. Uncertainty aware learning for high energy physics. Preprint at arXiv https://arxiv.org/abs/2105.08742 (2021).

  106. Rogozhnikov, A. Reweighting with boosted decision trees. Proc. Int. Workshop Adv. Comput. Anal. Tech. Phys. Res. 762, 012036 (2016).

    Google Scholar 

  107. Andreassen, A. & Nachman, B. Neural networks for full phase-space reweighting and parameter tuning. Phys. Rev. D 101, 091901 (2020).

    Article  ADS  Google Scholar 

  108. Cranmer, K., Pavez, J. & Louppe, G. Approximating likelihood ratios with calibrated discriminative classifiers. Preprint at arXiv https://arxiv.org/abs/1506.02169 (2015).

  109. Diefenbacher, S. et al. DCTRGAN: improving the precision of generative models with reweighting. J. Instrum. 15, P11004 (2020).

    Article  Google Scholar 

  110. Nachman, B. & Thaler, J. Neural conditional reweighting. Preprint at arXiv https://arxiv.org/abs/2107.08979 (2021).

  111. Clavijo, J. M., Glaysher, P. & Katzy, J. M. Adversarial domain adaptation to reduce sample bias of a high energy physics classifier. Preprint at arXiv https://arxiv.org/abs/2005.00568 (2020).

  112. Perdue, G. N. et al. Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment. J. Instrum. 13, P11020 (2018).

    Article  Google Scholar 

  113. Lin, J., Bhimji, W. & Nachman, B. Machine learning templates for QCD factorization in the search for physics beyond the standard model. J. High Energy Phys. 05, 181 (2019).

    Article  ADS  MathSciNet  Google Scholar 

  114. Kasieczka, G., Nachman, B., Schwartz, M. D. & Shih, D. Automating the ABCD method with machine learning. Phys. Rev. D 103, 035021 (2021).

    Article  ADS  MathSciNet  Google Scholar 

  115. Mikuni, V., Nachman, B. & Shih, D. Online-compatible unsupervised non-resonant anomaly detection. Preprint at arXiv https://arxiv.org/abs/2111.06417 (2021).

  116. Blance, A., Spannowsky, M. & Waite, P. Adversarially-trained autoencoders for robust unsupervised new physics searches. J. High Energy Phys. 10, 047 (2019).

    Article  ADS  Google Scholar 

  117. Englert, C., Galler, P., Harris, P. & Spannowsky, M. Machine learning uncertainties with adversarial neural networks. Eur. Phys. J. C 79, 4 (2019).

    Article  ADS  Google Scholar 

  118. Louppe, G., Kagan, M. & Cranmer, K. Learning to pivot with adversarial networks. Adv. Neural Inf. Process. Syst. 30, 981–990 (2017).

    Google Scholar 

  119. Dolen, J., Harris, P., Marzani, S., Rappoccio, S. & Tran, N. Thinking outside the ROCs: designing decorrelated taggers (DDT) for jet substructure. J. High Energy Phys. 05, 156 (2016).

    Article  ADS  Google Scholar 

  120. Moult, I., Nachman, B. & Neill, D. Convolved substructure: analytically decorrelating jet substructure observables. J. High Energy Phys. 05, 002 (2018).

    Article  ADS  Google Scholar 

  121. Stevens, J. & Williams, M. uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers. J. Instrum. 8, P12013 (2013).

    Article  Google Scholar 

  122. Shimmin, C. et al. Decorrelated jet substructure tagging using adversarial neural networks. Phys. Rev. D 96, 074034 (2017).

    Article  ADS  Google Scholar 

  123. Bradshaw, L., Mishra, R. K., Mitridate, A. & Ostdiek, B. Mass agnostic jet taggers. SciPost Phys. 8, 011 (2020).

    Article  ADS  Google Scholar 

  124. ATLAS collaboration. Performance of mass-decorrelated jet substructure observables for hadronic two-body decay tagging in ATLAS. Report ATL-PHYS-PUB-2018-014 (CERN, 2018).

  125. Kasieczka, G. & Shih, D. Robust jet classifiers through distance correlation. Phys. Rev. Lett. 125, 122001 (2020).

    Article  ADS  Google Scholar 

  126. Wunsch, S., Jörger, S., Wolf, R. & Quast, G. Reducing the dependence of the neural network function to systematic uncertainties in the input space. Comput. Softw. Big Sci. 4, 5 (2020).

    Article  Google Scholar 

  127. Rogozhnikov, A., Bukva, A., Gligorov, V. V., Ustyuzhanin, A. & Williams, M. New approaches for boosting to uniformity. J. Instrum. 10, T03002 (2015).

    Article  Google Scholar 

  128. CMS Collaboration. A deep neural network to search for new long-lived particles decaying to jets. Mach. Learn. Sci. Technol. 1, 035012 (2020).

    Article  Google Scholar 

  129. Kitouni, O., Nachman, B., Weisser, C. & Williams, M. Enhancing searches for resonances with machine learning and moment decomposition. Preprint at arXiv https://arxiv.org/abs/2010.09745 (2020).

  130. Estrade, V., Germain, C., Guyon, I. & Rousseau, D. Systematic aware learning — a case study in high energy physics. EPJ Web Conf. 214, 06024 (2019).

    Article  Google Scholar 

  131. Aguilar-Saavedra, J. A., Collins, J. H. & Mishra, R. K. A generic anti-QCD jet tagger. J. High Energy Phys. 11, 163 (2017).

    Article  ADS  Google Scholar 

  132. Aguilar-Saavedra, J. A., Joaquim, F. R. & Seabra, J. F. Mass unspecific supervised tagging (MUST) for boosted jets. J. High Energy Phys. 03, 012 (2021).

    Article  ADS  Google Scholar 

  133. Ghosh, A. & Nachman, B. A cautionary tale of decorrelating theory uncertainties. Preprint at arXiv https://arxiv.org/abs/2109.08159 (2021).

  134. Chouldechova, A. & Roth, A. The frontiers of fairness in machine learning. Preprint at arXiv https://arxiv.org/abs/1810.08810 (2018).

  135. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. Preprint at arXiv https://arxiv.org/abs/1908.09635 (2019).

  136. Frate, M., Cranmer, K., Kalia, S., Vandenberg-Rodes, A. & Whiteson, D. Modeling smooth backgrounds and generic localized signals with Gaussian processes. Preprint at arXiv https://arxiv.org/abs/1709.05681 (2017).

  137. Di Sipio, R., Faucci Giannelli, M., Ketabchi Haghighat, S. & Palazzo, S. DijetGAN: a generative-adversarial network approach for the simulation of QCD dijet events at the LHC. J. High Energy Phys. 08, 110 (2019).

    Article  ADS  Google Scholar 

  138. Chisholm, A. et al. Non-parametric data-driven background modelling using conditional probabilities. Preprint at arXiv https://arxiv.org/abs/2112.00650 (2021).

  139. Neyman, J. & Pearson, E. S. On the problem of the most efficient tests of statistical hypotheses. Phil. Trans. R. Soc. Lond. A 231, 289 (1933).

    Article  ADS  MATH  Google Scholar 

  140. Kasieczka, G. et al. The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics. Preprint at arXiv https://arxiv.org/abs/2101.08320 (2021).

  141. Aarrestad, T. et al. The Dark Machines anomaly score challenge: benchmark data and model independent event classification for the Large Hadron Collider. Preprint at arXiv https://arxiv.org/abs/2105.14027 (2021).

  142. Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006).

    Article  ADS  MathSciNet  MATH  Google Scholar 

  143. Finke, T., Krämer, M., Morandini, A., Mück, A. & Oleksiyuk, I. Autoencoders for unsupervised anomaly detection in high energy physics. Preprint at arXiv https://arxiv.org/abs/2104.09051 (2021).

  144. Dillon, B. M., Plehn, T., Sauer, C. & Sorrenson, P. Better latent spaces for better autoencoders. Preprint at arXiv https://arxiv.org/abs/2104.08291 (2021).

  145. Batson, J., Haaf, C. G., Kahn, Y. & Roberts, D. A. Topological obstructions to autoencoding. Preprint at arXiv https://arxiv.org/abs/2102.08380 (2021).

  146. Fraser, K., Homiller, S., Mishra, R. K., Ostdiek, B. & Schwartz, M. D. Challenges for unsupervised anomaly detection in particle physics. Preprint at arXiv https://arxiv.org/abs/2110.06948 (2021).

  147. Cerri, O., Nguyen, T. Q., Pierini, M., Spiropulu, M. & Vlimant, J.-R. Variational autoencoders for new physics mining at the Large Hadron Collider. J. High Energy Phys. 05, 036 (2019).

    Article  ADS  Google Scholar 

  148. Govorkova, E. et al. Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider. Preprint at arXiv https://arxiv.org/abs/2108.03986 (2021).

  149. Crispim Romão, M., Castro, N. F. & Pedro, R. Finding new physics without learning about it: anomaly detection as a tool for searches at colliders. Eur. Phys. J. C 81, 27 (2021).

    Article  ADS  Google Scholar 

  150. Dillon, B. M., Faroughy, D. A. & Kamenik, J. F. Uncovering latent jet substructure. Phys. Rev D. 100, 056002 (2019).

    Article  ADS  Google Scholar 

  151. Caron, S., Hendriks, L. & Verheyen, R. Rare and different: anomaly scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC. Preprint at arXiv https://arxiv.org/abs/2106.10164 (2021).

  152. Mikuni, V. & Canelli, F. Unsupervised clustering for collider physics. Preprint at arXiv https://arxiv.org/abs/2010.07106v3 (2020).

  153. Knapp, O. et al. Adversarially learned anomaly detection on CMS open data: re-discovering the top quark. Eur. Phys. J. Plus 136, 236 (2021).

    Article  Google Scholar 

  154. Amram, O. & Suarez, C. M. Tag N’ Train: a technique to train improved classifiers on unlabeled data. J. High Energy Phys. 01, 153 (2021).

    Article  ADS  Google Scholar 

  155. Collins, J. H., Martín-Ramiro, P., Nachman, B. & Shih, D. Comparing weak- and unsupervised methods for resonant anomaly detection. Eur. Phys. J. C 81, 617 (2021).

    Article  ADS  Google Scholar 

  156. Collins, J. H., Howe, K. & Nachman, B. Anomaly detection for resonant new physics with machine learning. Phys. Rev. Lett. 121, 241803 (2018).

    Article  ADS  Google Scholar 

  157. Collins, J. H., Howe, K. & Nachman, B. Extending the search for new resonances with machine learning. Phys. Rev. D 99, 014038 (2019).

    Article  ADS  Google Scholar 

  158. D’Agnolo, R. T. & Wulzer, A. Learning new physics from a machine. Phys. Rev. D 99, 015014 (2019).

    Article  ADS  Google Scholar 

  159. D’Agnolo, R. T., Grosso, G., Pierini, M., Wulzer, A. & Zanetti, M. Learning multivariate new physics. Eur. Phys. J. C 81, 89 (2021).

    Article  ADS  Google Scholar 

  160. d’Agnolo, R. T., Grosso, G., Pierini, M., Wulzer, A. & Zanetti, M. Learning new physics from an imperfect machine. Preprint at arXiv https://arxiv.org/abs/2111.13633 (2021).

  161. Andreassen, A., Nachman, B. & Shih, D. Simulation assisted likelihood-free anomaly detection. Phys. Rev. D 101, 095004 (2020).

    Article  ADS  Google Scholar 

  162. Benkendorfer, K., Pottier, L. L. & Nachman, B. Simulation-assisted decorrelation for resonant anomaly detection. Phys. Rev. D 104, 035003 (2021).

    Article  ADS  MathSciNet  Google Scholar 

  163. Park, S. E., Rankin, D., Udrescu, S.-M., Yunus, M. & Harris, P. Quasi anomalous knowledge: searching for new physics with embedded knowledge. J. High Energy Phys. 21, 030 (2020).

    Google Scholar 

  164. Khosa, C. K. & Sanz, V. Anomaly awareness. Preprint at arXiv https://arxiv.org/abs/2007.14462 (2020).

  165. Stein, G., Seljak, U. & Dai, B. Unsupervised in-distribution anomaly detection of new physics through conditional density estimation. Preprint at arXiv https://arxiv.org/abs/2012.11638 (2020).

  166. Hallin, A. et al. Classifying Anomalies THrough Outer Density Estimation (CATHODE). Preprint at arXiv https://arxiv.org/abs/2109.00546 (2021).

  167. Low, J. F. et al. Boosted decision trees in the CMS level-1 endcap muon trigger. Proc. Sci. 2017, 143 (2017).

    Google Scholar 

  168. Gligorov, V. V. & Williams, M. Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree. J. Instrum. 8, P02013 (2013).

    Article  Google Scholar 

  169. Aaij, R. et al. The LHCb trigger and its performance in 2011. J. Instrum. 8, P04022 (2013).

    Article  Google Scholar 

  170. Duarte, J. et al. Fast inference of deep neural networks in FPGAs for particle physics. J. Instrum. 13, P07027 (2018).

    Article  Google Scholar 

  171. Nottbeck, N., Schmitt, C. & Büscher, V. Implementation of high-performance, sub-microsecond deep neural networks on FPGAs for trigger applications. J. Instrum. 14, P09014 (2019).

    Article  Google Scholar 

  172. Zabi, A., Berryhill, J. W., Perez, E. & Tapper, A. D. The phase-2 upgrade of the CMS level-1 trigger. Interim Technical Design Report CMS-TDR-017 (CERN, 2020).

  173. Summers, S. et al. Fast inference of boosted decision trees in FPGAs for particle physics. J. Instrum. 15, P05026 (2020).

    Article  Google Scholar 

  174. Aarrestad, T. et al. Fast convolutional neural networks on FPGAs with hls4ml. Mach. Learn. Sci. Tech. 2, 045015 (2021).

    Article  Google Scholar 

  175. Hong, T. M. et al. Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics. J. Instrum. 16, P08016 (2021).

    Article  Google Scholar 

  176. LHCb Collaboration. LHCb upgrade GPU high level trigger technical design report. CERN-LHCC-2020-006 LHCB-TDR-021 (CERN, 2020).

  177. Aaij, R. et al. Allen: a high level trigger on GPUs for LHCb. Comput. Softw. Big Sci. 4, 7 (2020).

    Article  Google Scholar 

  178. Chekalina, V. et al. Generative models for fast calorimeter simulation: the LHCb case. EPJ Web Conf. 214, 02034 (2019).

    Article  Google Scholar 

  179. ATLAS collaboration. Fast simulation of the ATLAS calorimeter system with generative adversarial networks. Report ATL-SOFT-PUB-2020-006 (CERN, 2020).

  180. Aad, G. et al. AtlFast3: the next generation of fast simulation in ATLAS. Preprint at arXiv https://arxiv.org/abs/2109.02551 (2021).

  181. Aad, G. et al. ATLAS b-jet identification performance and efficiency measurement with \(t\bar{t}\) events in pp collisions at \(\sqrt{s}=13\) TeV. Eur. Phys. J. C 79, 970 (2019).

    Article  ADS  Google Scholar 

  182. Bols, E., Kieseler, J., Verzetti, M., Stoye, M. & Stakia, A. Jet flavour classification using DeepJet. J. Instrum. 15, P12012 (2020).

    Article  Google Scholar 

  183. ATLAS collaboration. Deep sets based neural networks for impact parameter flavour tagging in ATLAS. Report ATL-PHYS-PUB-2020-014 (CERN, 2020).

  184. Larkoski, A. J., Moult, I. & Nachman, B. Jet substructure at the Large Hadron Collider: a review of recent advances in theory and machine learning. Phys. Rep. 841, 1–63 (2020).

    Article  ADS  Google Scholar 

  185. Kogler, R. et al. Jet substructure at the Large Hadron Collider: experimental review. Rev. Mod. Phys. 91, 045003 (2019).

    Article  ADS  Google Scholar 

  186. Sirunyan, A. M. et al. Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. J. Instrum. 15, P06005 (2020).

    Article  Google Scholar 

  187. Sirunyan, A. M. et al. Search for dark matter particles produced in association with a Higgs boson in proton–proton collisions at \(\sqrt{{\rm{s}}}\) = 13 TeV. J. High Energy Phys. 03, 025 (2020).

    Google Scholar 

  188. CMS Collaboration. Search for resonant Higgs boson pair production in four b quark final state using large-area jets in proton–proton collisions at \(\sqrt{s}=13\,{\rm{TeV}}\). Technical Report CMS-PAS-B2G-20-004 (CERN, 2021).

  189. CMS Collaboration. Search for heavy resonances decaying to a pair of boosted Higgs bosons in final states with leptons and a bottom quark–antiquark pair at \(\sqrt{s}=13\) TeV. Technical Report CMS-PAS-B2G-20-007 (CERN, 2021).

  190. CMS Collaboration. Search for Higgs boson pair production via vector boson fusion with highly Lorentz-boosted Higgs bosons in the four b quark final state at \(\sqrt{s}=13\) TeV. Technical Report CMS-PAS-B2G-21-001 (CERN, 2021).

  191. Sirunyan, A. M. et al. Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV. J. Instrum. 13, P05011 (2018).

    Article  Google Scholar 

  192. Sirunyan, A. M. et al. Search for W′ bosons decaying to a top and a bottom quark at √s = 13 TeV in the hadronic final state. Phys. Lett. B 820, 136535 (2021).

    Article  Google Scholar 

  193. ATLAS collaboration. Efficiency corrections for a tagger for boosted \(H\to b\bar{b}\) decays in pp collisions at \(\sqrt{s}=13\) TeV with the ATLAS detector. Technical Report ATL-PHYS-PUB-2021-035 (2021).

  194. Sirunyan, A. M. et al. A deep neural network to search for new long-lived particles decaying to jets. Mach. Learn. Sci. Tech. 1, 035012 (2020).

    Article  Google Scholar 

  195. CMS Collaboration. Identification of highly Lorentz-boosted heavy particles using graph neural networks and new mass decorrelation techniques. Report CMS-DP-2020-002 (CERN, 2020).

  196. Tumasyan, A. et al. Search for new particles in events with energetic jets and large missing transverse momentum in proton–proton collisions at \(\sqrt{s}=\) 13 TeV. Preprint at arXiv https://arxiv.org/abs/2107.13021 (2021).

  197. Aaij, R. et al. Search for heavy neutral leptons in W+ → μ+μ±jet decays. Eur. Phys. J. C 81, 248 (2021).

    Article  ADS  Google Scholar 

  198. Aad, G. et al. Dijet resonance search with weak supervision using \(\sqrt{s}=13\) TeV pp collisions in the ATLAS detector. Phys. Rev. Lett. 125, 131801 (2020).

    Article  ADS  Google Scholar 

  199. Aaboud, M. et al. Search for pair production of higgsinos in final states with at least three b-tagged jets in \(\sqrt{s}=13\) TeV pp collisions using the ATLAS detector. Phys. Rev. D 98, 092002 (2018).

    Article  ADS  Google Scholar 

  200. Aad, G. et al. Search for Higgs boson decays into two new low-mass spin-0 particles in the 4b channel with the ATLAS detector using pp collisions at \(\sqrt{s}=13\) TeV. Phys. Rev. D 105, 012006 (2022).

    Article  ADS  Google Scholar 

  201. Aad, G. et al. Search for heavy resonances decaying into a pair of Z bosons in the \({\ell }^{+}{\ell }^{-}{\ell {}^{{\prime} }}^{+}{\ell {}^{{\prime} }}^{-}\) and \({\ell }^{+}{\ell }^{-}{\ell {}^{{\prime} }}^{+}{\ell {}^{{\prime} }}^{-}\) final states using 139 fb−1 of proton–proton collisions at \({\ell }^{+}{\ell }^{-}{\ell {}^{{\prime} }}^{+}{\ell {}^{{\prime} }}^{-}\)TeV with the ATLAS detector. Eur. Phys. J. C 81, 332 (2021).

    Article  ADS  Google Scholar 

  202. Tumasyan, A. et al. Search for a heavy Higgs boson decaying into two lighter Higgs bosons in the ττbb final state at 13 TeV. Preprint at arXiv https://arxiv.org/abs/2106.10361 (2021).

  203. Aad, G. et al. Search for Higgs boson decays into a Z boson and a light hadronically decaying resonance using 13 TeV pp collision data from the ATLAS detector. Phys. Rev. Lett. 125, 221802 (2020).

    Article  ADS  Google Scholar 

  204. Aad, G. et al. Search for dark matter in events with missing transverse momentum and a Higgs boson decaying into two photons in pp collisions at \(\sqrt{s}=13\) TeV with the ATLAS detector. Preprint at arXiv https://arxiv.org/abs/2104.13240 (2021).

  205. Chen, T. & Guestrin, C. in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining 785–794 (ACM, 2016).

  206. Bertacchi, V. et al. Track finding at Belle II. Comput. Phys. Commun. 259, 107610 (2021).

    Article  Google Scholar 

  207. Abazajian, K. N. et al. Light sterile neutrinos: a white paper. Preprint at arXiv https://arxiv.org/abs/1204.5379 (2012).

  208. Dentler, M. et al. Updated global analysis of neutrino oscillations in the presence of eV-scale sterile neutrinos. J. High Energy Phys. 08, 010 (2018).

    Article  ADS  Google Scholar 

  209. Bertuzzo, E., Jana, S., Machado, P. A. N. & Zukanovich Funchal, R. Dark neutrino portal to explain MiniBooNE excess. Phys. Rev. Lett. 121, 241801 (2018).

    Article  ADS  Google Scholar 

  210. Ballett, P., Pascoli, S. & Ross-Lonergan, M. U(1)′ mediated decays of heavy sterile neutrinos in MiniBooNE. Phys. Rev. D 99, 071701 (2019).

    Article  ADS  Google Scholar 

  211. Adamson, P. et al. Constraints on large extra dimensions from the MINOS experiment. Phys. Rev. D 94, 111101 (2016).

    Article  ADS  Google Scholar 

  212. Kostelecky, V. A. & Mewes, M. Lorentz and CPT violation in neutrinos. Phys. Rev. D 69, 016005 (2004).

    Article  ADS  Google Scholar 

  213. Miranda, O. G. & Nunokawa, H. Non standard neutrino interactions: current status and future prospects. New J. Phys. 17, 095002 (2015).

    Article  ADS  Google Scholar 

  214. de Gouvêa, A. & Kelly, K. J. Non-standard neutrino interactions at DUNE. Nucl. Phys. B 908, 318–335 (2016).

    Article  ADS  Google Scholar 

  215. Jwa, Y.-J., Guglielmo, G. D., Carloni, L. P. & Karagiorgi, G. in 2019 New York Sci. Data Summit (IEEE, 2019).

  216. Acciarri, R. et al. A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber. Preprint at arXiv https://arxiv.org/abs/2103.06391 (2021).

  217. Uboldi, L. et al. Extracting low energy signals from raw LArTPC waveforms using deep learning techniques–a proof of concept. Preprint at arXiv https://arxiv.org/abs/2106.09911 (2021).

  218. Anker, A. et al. A novel trigger based on neural networks for radio neutrino detectors. Proc. Sci. 395, 1074 (2021).

    Google Scholar 

  219. Acero, M. A. et al. Search for active-sterile antineutrino mixing using neutral-current interactions with the NOvA experiment. Phys. Rev. Lett. 127, 201801 (2021).

    Article  ADS  Google Scholar 

  220. Abratenko, P. et al. Search for an anomalous excess of charged-current quasi-elastic νe interactions with the MicroBooNE experiment using deep-learning-based reconstruction. Preprint at arXiv https://arxiv.org/abs/2110.14080 (2021).

  221. Baldi, P., Bian, J., Hertel, L. & Li, L. Improved energy reconstruction in NOvA with regression convolutional neural networks. Phys. Rev. D 99, 012011 (2019).

    Article  ADS  Google Scholar 

  222. Abratenko, P. et al. Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation. Preprint at arXiv https://arxiv.org/abs/2110.13961 (2021).

  223. Aiello, S. et al. Event reconstruction for KM3NeT/ORCA using convolutional neural networks. J. Instrum. 15, P10005 (2020).

    Article  Google Scholar 

  224. Ayres, D. S. et al. The NOvA technical design report. FERMILAB-DESIGN-2007-01 (OSTI, 2007).

  225. Psihas, F., Groh, M., Tunnell, C. & Warburton, K. A review on machine learning for neutrino experiments. Int. J. Mod. Phys. A 35, 2043005 (2020).

    Article  ADS  Google Scholar 

  226. Abi, B. et al. Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report Vol. I: Introduction to DUNE. J. Instrum. 15, T08008 (2020).

    Article  Google Scholar 

  227. Acciarri, R. et al. Design and construction of the MicroBooNE detector. J. Instrum. 12, P02017 (2017).

    Article  Google Scholar 

  228. Antonello, M. et al. A proposal for a three detector short-baseline neutrino oscillation program in the Fermilab Booster Neutrino Beam. Preprint at arXiv https://arxiv.org/abs/1503.01520 (2015).

  229. Abi, B. et al. Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Phys. Rev. D 102, 092003 (2020).

    Article  ADS  Google Scholar 

  230. Liu, J. et al. Deep-learning-based kinematic reconstruction for DUNE. Preprint at arXiv https://arxiv.org/abs/2012.06181 (2020).

  231. Acciarri, R. et al. Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber. J. Instrum. 12, P03011 (2017).

    Article  Google Scholar 

  232. Adams, C. et al. Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber. Phys. Rev. D 99, 092001 (2019).

    Article  ADS  Google Scholar 

  233. Ronneberger, O., Fischer, P. & Brox, T. U-net: convolutional networks for biomedical image segmentation. Preprint at arXiv https://arxiv.org/abs/1505.04597 (2015).

  234. Abratenko, P. et al. Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE. Phys. Rev. D 103, 052012 (2021).

    Article  ADS  Google Scholar 

  235. Dominé, L. & Terao, K. Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data. Phys. Rev. D 102, 012005 (2020).

    Article  ADS  Google Scholar 

  236. Acciarri, R. et al. Cosmic background removal with deep neural networks in SBND. Preprint at arXiv https://arxiv.org/abs/2012.01301 (2020).

  237. Drielsma, F., Terao, K., Dominé, L. & Koh, D. H. Scalable, end-to-end, deep-learning-based data reconstruction chain for particle imaging detectors. Preprint at arXiv https://arxiv.org/abs/2102.01033 (2021).

  238. Dominé, L. & Terao, K. Point proposal network for reconstructing 3D particle positions with sub-pixel precision in liquid argon time projection chambers. Preprint at arXiv https://arxiv.org/abs/2006.14745 (2020).

  239. Koh, D. H. et al. Scalable, proposal-free instance segmentation network for 3d pixel clustering and particle trajectory reconstruction in liquid argon time projection chambers. Preprint at arXiv https://arxiv.org/abs/2007.03083 (2020).

  240. Drielsma, F. et al. Clustering of electromagnetic showers and particle interactions with graph neural networks in liquid argon time projection chambers. Phys. Rev. D 104, 072004 (2021).

    Article  ADS  Google Scholar 

  241. Adams, C., Terao, K. & Wongjirad, T. PILArNet: public dataset for particle imaging liquid argon detectors in high energy physics. Preprint at arXiv https://arxiv.org/abs/2006.01993 (2020).

  242. Psihas, F. The convolutional visual network for identification and reconstruction of NOvA events. J. Phys. Conf. Ser. 898, 072053 (2017).

    Article  Google Scholar 

  243. Gando, A. et al. Search for Majorana neutrinos near the inverted mass hierarchy region with KamLAND-Zen. Phys. Rev. Lett. 117, 082503 (2016); addendum 117, 109903 (2016).

    Article  ADS  Google Scholar 

  244. Racah, E. et al. Revealing fundamental physics from the Daya Bay neutrino experiment using deep neural networks. Preprint at arXiv https://arxiv.org/abs/1601.07621 (2016).

  245. Choma, N. et al. Graph neural networks for IceCube signal classification. Preprint at arXiv https://arxiv.org/abs/1809.06166 (2018).

  246. Abbasi, R. et al. Reconstruction of neutrino events in IceCube using graph neural networks. Proc. Sci. 395, 1044 (2021).

    Google Scholar 

  247. Abi, B. et al. Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Vol. III: DUNE far detector technical coordination. J. Instrum. 15, T08009 (2020).

    Article  Google Scholar 

  248. Wang, M. et al. GPU-accelerated machine learning inference as a service for computing in neutrino experiments. Front. Big Data 3, 604083 (2021).

    Article  Google Scholar 

  249. Akerib, D. S. et al. Improving sensitivity to low-mass dark matter in LUX using a novel electrode background mitigation technique. Phys. Rev. D 104, 012011 (2021).

    Article  ADS  Google Scholar 

  250. Akerib, D. S. et al. Constraints on effective field theory couplings using 311.2 days of LUX data. Preprint at arXiv https://arxiv.org/abs/2102.06998 (2021).

  251. Rossiter, P. Background Mitigation in Dual Phase Xenon Time Projection Chambers. Thesis, Sheffield Univ. (2021).

  252. Aprile, E. et al. Search for coherent elastic scattering of solar 8B neutrinos in the XENON1T dark matter experiment. Phys. Rev. Lett. 126, 091301 (2021).

    Article  ADS  Google Scholar 

  253. Agnese, R. et al. Search for low-mass dark matter with CDMSlite using a profile likelihood fit. Phys. Rev. D 99, 062001 (2019).

    Article  ADS  Google Scholar 

  254. Adhikari, G. et al. Lowering the energy threshold in COSINE-100 dark matter searches. Astropart. Phys. 130, 102581 (2021).

    Article  Google Scholar 

  255. Albert, J. B. et al. Search for 2νββ decay of 136Xe to the 0\({}_{1}^{+}\) excited state of 136Ba with EXO-200. Phys. Rev. C 93, 035501 (2016).

    Article  ADS  Google Scholar 

  256. Albert, J. B. et al. Search for neutrinoless double-beta decay with the upgraded EXO-200 detector. Phys. Rev. Lett. 120, 072701 (2018).

    Article  ADS  Google Scholar 

  257. Anton, G. et al. Search for neutrinoless double-β decay with the complete EXO-200 dataset. Phys. Rev. Lett. 123, 161802 (2019).

    Article  ADS  Google Scholar 

  258. Yu, T. C. Template-free pulse height estimation of microcalorimeter responses with PCA. Preprint at arXiv https://arxiv.org/abs/1910.14261 (2019).

  259. Wagner, F. Machine Learning Methods for the Raw Data Analysis of Cryogenic Dark Matter Experiments. Thesis, TU Wien (2020).

  260. Holl, P. et al. Deep learning based pulse shape discrimination for germanium detectors. Eur. Phys. J. C 79, 450 (2019).

    Article  ADS  Google Scholar 

  261. Matusch, B. et al. Developing a bubble chamber particle discriminator using semi-supervised learning. Preprint at arXiv https://arxiv.org/abs/1811.11308 (2018).

  262. Matusch, B. & Cao, G. Particle identification using semi-supervised learning in the PICO-60 dark matter detector. J. Phys. Conf. Ser. 1525, 012085 (2020).

    Article  Google Scholar 

  263. Mühlmann, C. Pulse-Shape Discrimination with Deep Learning in CRESST. Thesis, TU Wien (2019).

  264. Ai, P., Wang, D., Huang, G. & Sun, X. Three-dimensional convolutional neural networks for neutrinoless double-beta decay signal/background discrimination in high-pressure gaseous time projection chamber. J. Instrum. 13, P08015 (2018).

    Article  Google Scholar 

  265. Renner, J. et al. Background rejection in NEXT using deep neural networks. J. Instrum. 12, T01004 (2017).

    Article  Google Scholar 

  266. Kekic, M. et al. Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. J. High Energy Phys. 01, 189 (2021).

    Article  Google Scholar 

  267. Li, Z. et al. Simulation of charge readout with segmented tiles in nEXO. J. Instrum. 14, P09020 (2019).

    Article  Google Scholar 

  268. Adhikari, G. et al. nEXO: neutrinoless double beta decay search beyond 1028 year half-life sensitivity. Preprint at arXiv https://arxiv.org/abs/2106.16243 (2021).

  269. Qiao, H. et al. Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation. Sci. China Phys. Mech. Astron. 61, 101007 (2018).

    Article  ADS  Google Scholar 

  270. Li, A., Elagin, A., Fraker, S., Grant, C. & Winslow, L. Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks. Nucl. Instrum. Meth. A 947, 162604 (2019).

    Article  Google Scholar 

  271. Golovatiuk, A., Ustyuzhanin, A., Alexandrov, A. & De Lellis, G. Deep learning for direct dark matter search with nuclear emulsions. Preprint at arXiv https://arxiv.org/abs/2106.11995 (2021).

  272. Simola, U., Pelssers, B., Barge, D., Conrad, J. & Corander, J. Machine learning accelerated likelihood-free event reconstruction in dark matter direct detection. J. Instrum. 14, P03004 (2019).

    Article  Google Scholar 

  273. Delaquis, S. et al. Deep neural networks for energy and position reconstruction in EXO-200. J. Instrum. 13, P08023 (2018).

    Article  Google Scholar 

  274. Aprile, E. et al. XENON1T dark matter data analysis: signal reconstruction, calibration and event selection. Phys. Rev. D 100, 052014 (2019).

    Article  ADS  Google Scholar 

  275. Goicoechea-Casanueva, V., Kish, A. & Maricic, J. Event vertex reconstruction with deep neural networks for the DarkSide-20k experiment. EPJ Web Conf. 251, 03029 (2021).

    Article  Google Scholar 

  276. Grobov, A. & Ilyasov, A. Convolutional neural network approach to event position reconstruction in DarkSide-50 experiment. J. Phys. Conf. Ser. 1690, 012013 (2020).

    Article  Google Scholar 

  277. Ashtari Esfahani, A. et al. Cyclotron radiation emission spectroscopy signal classification with machine learning in Project 8. New J. Phys. 22, 033004 (2020).

    Article  Google Scholar 

  278. Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous systems. Preprint at arXiv https://arxiv.org/abs/1603.04467 (2016).

  279. Paszke, A. et al. Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8024–8035 (2019).

    Google Scholar 

  280. Lindegren, L. et al. Gaia Data Release 2. The astrometric solution. Astron. Astrophys. 616, A2 (2018).

    Article  Google Scholar 

  281. Abbott, B. P. et al. LIGO: the Laser Interferometer Gravitational-wave Observatory. Rep. Prog. Phys. 72, 076901 (2009).

    Article  ADS  Google Scholar 

  282. Ivezić, Ž. et al. LSST: from science drivers to reference design and anticipated data products. Astrophys. J. 873, 111 (2019).

    Article  ADS  Google Scholar 

  283. Weltman, A. et al. Fundamental physics with the Square Kilometre Array. Publ. Astron. Soc. Aust. 37, e002 (2020).

    Article  ADS  Google Scholar 

  284. Stein, G. georgestein/ml-in-cosmology: machine learning in cosmology. zenodo https://doi.org/10.5281/zenodo.4024768 (2020).

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Acknowledgements

S.K. and B.N. are supported by the US Department of Energy (DOE) Office of Science under contract DE-AC02-05CH11231. G. Kasieczka acknowledges the support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy — EXC 2121 ‘Quantum Universe’ — 390833306. The work of D.S. was supported by DOE grant DOE-SC0010008. G. Karagiorgi is supported by the US National Science Foundation under grant no. PHY-1753228.

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Authors and Affiliations

  1. Department of Physics, Columbia University, New York, NY, USA

    Georgia Karagiorgi

  2. Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany

    Gregor Kasieczka

  3. Physics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Scott Kravitz & Benjamin Nachman

  4. Berkeley Institute for Data Science, University of California, Berkeley, CA, USA

    Benjamin Nachman

  5. NHETC, Department of Physics and Astronomy, Rutgers University, Piscataway, NJ, USA

    David Shih

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  1. Georgia Karagiorgi
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The authors contributed equally to all aspects of the article.

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Correspondence to Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz, Benjamin Nachman or David Shih.

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Karagiorgi, G., Kasieczka, G., Kravitz, S. et al. Machine learning in the search for new fundamental physics. Nat Rev Phys 4, 399–412 (2022). https://doi.org/10.1038/s42254-022-00455-1

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