Craven, Sean and Croon, Djuna and Cutting, Daniel and Houtz, Rachel (2022) 'Machine learning a manifold.', Physical Review D, 105 (9).
We propose a simple method to identify a continuous Lie algebra symmetry in a dataset through regression by an artificial neural network. Our proposal takes advantage of the Oðϵ2Þ scaling of the output variable under infinitesimal symmetry transformations on the input variables. As symmetry transformations are generated post-training, the methodology does not rely on sampling of the full representation space or binning of the dataset, and the possibility of false identification is minimized. We demonstrate our method in the SU(3)-symmetric (non-) linear Σ model.
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|Publisher Web site:||https://doi.org/10.1103/PhysRevD.105.096030|
|Publisher statement:||Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.|
|Date accepted:||04 May 2022|
|Date deposited:||26 July 2022|
|Date of first online publication:||25 May 2022|
|Date first made open access:||26 July 2022|
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