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Lessons on interpretable machine learning from particle physics

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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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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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).

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