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Symmetry Discovery with Deep Learning
Krish Desai · Benjamin Nachman · Jesse Thaler

Symmetries are a fundamental property of functions associated with data. A key function for any dataset is its probability density, and the symmetries thereof are referred to as the symmetries of the dataset itself. We provide a rigorous statistical notion of symmetry for a dataset, which involves reference datasets that we call "inertial" in analogy to inertial frames in classical mechanics. Then, we construct a novel approach to automatically discover symmetries from a dataset using a deep learning method based on an adversarial neural network. We test our method on the LHC Olympics dataset. Symmetry discovery may lead to new insights and can reduce the effective dimensionality of a dataset to increase its effective statistics.

Author Information

Krish Desai (University of California, Berkeley)
Benjamin Nachman (Lawrence Berkeley National Laboratory)
Jesse Thaler (MIT)

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