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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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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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