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Lessons on interpretable machine learning from particle physics

Nature Reviews Physics volume 4, pages 284–286 (2022)Cite this article

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Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Christophe Grojean, Ayan Paul, Zhuoni Qian and Inga Strümke give an overview of how to introduce interpretability to methods commonly used in particle physics.

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Fig. 1: 12-variable machine learning-assisted analysis for classifying five particle-production channels.

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Acknowledgements

This work benefited from support by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy EXC 2121 “Quantum Universe”–390833306. The work of A.P. is funded by Volkswagen Foundation within the initiative “Corona Crisis and Beyond–Perspectives for Science, Scholarship and Society”. I.S. is grateful to the Norwegian Research Council for support through the EXAIGON project–Explainable AI systems for gradual industry adoption (grant no. 304843).

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Authors and Affiliations

  1. Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany

    Christophe Grojean & Ayan Paul

  2. Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany

    Christophe Grojean & Ayan Paul

  3. School of Physics, Hangzhou Normal University, Hangzhou, Zhejiang, China

    Zhuoni Qian

  4. Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway

    Inga Strümke

Authors
  1. Christophe Grojean
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  2. Ayan Paul
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  3. Zhuoni Qian
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  4. Inga Strümke
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Correspondence to Ayan Paul.

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Grojean, C., Paul, A., Qian, Z. et al. Lessons on interpretable machine learning from particle physics. Nat Rev Phys 4, 284–286 (2022). https://doi.org/10.1038/s42254-022-00456-0

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