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arXiv 2006.04780hep-phcs.LGhep-exphysics.comp-phstat.ML

Lorentz Group Equivariant Neural Network for Particle Physics

University of Chicago(芝加哥大学);Atomwise(Atomwise公司);Enrico Fermi Institute(恩里科·费米研究所);Flatiron Institute(熨斗研究所)

Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann, Marwah Roussi, David W. Miller, Risi Kondor

英文摘要:

We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The architecture is based on the theory of the finite-dimensional representations of the Lorentz group and the equivariant nonlinearity involves the tensor product. For classification tasks in particle physics, we demonstrate that such an equivariant architecture leads to drastically simpler models that have relatively few learnable parameters and are much more physically interpretable than leading approaches that use CNNs and point cloud approaches. The competitive performance of the network is demonstrated on a public classification dataset [27] for tagging top quark decays given energy-momenta of jet constituents produced in proton-proton collisions.

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