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Symmetries and self-supervision in particle physics
Barry M Dillon · Tilman Plehn · Gregor Kasieczka

A long-standing problem in the design of machine-learning tools for particle physics applications has been how to incorporate prior knowledge of physical symmetries. In this note we propose contrastive self-supervision as a solution to this problem, with jet physics as an example. Using a permutation-invariant transformer network, we learn a representation which outperforms hand-crafted competitors on a linear classification benchmark.

Author Information

Barry M Dillon (University of Heidelberg)
Tilman Plehn (Heidelberg)
Gregor Kasieczka (Universit├Ąt Hamburg)

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