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Machine learning at the energy and intensity frontiers of particle physics


Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.

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Nature thanks C. Backhouse, M. Pierini and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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All authors contributed to writing this Review. A.R. and M.W. were the principal editors.

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The authors declare no competing interests.

Correspondence to Alexander Radovic or Mike Williams.

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Further reading

Fig. 1: Machine learning for calorimetry at CMS.
Fig. 2: Separating signal events from background in the ATLAS experiment.
Fig. 3: Neutrino selection and isolation in MicroBooNE.
Fig. 4: Exploring NOvA’s event-selection neural network using t-SNE.


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