Unsupervised Machine Learning of Quenched Gauge Symmetries: A Proof-of-Concept Demonstration [article]

Daniel Lozano-Gómez, Darren Pereira, Michel J. P. Gingras
2020 arXiv   pre-print
In condensed matter physics, one of the goals of machine learning is the classification of phases of matter. The consideration of a system's symmetries can significantly assist the machine in this goal. We demonstrate the ability of an unsupervised machine learning protocol, the Principal Component Analysis method, to detect hidden quenched gauge symmetries introduced via the so-called Mattis gauge transformation. Our work reveals that unsupervised machine learning can identify hidden
more » ... of a model and may therefore provide new insights into the models themselves.
arXiv:2003.00039v1 fatcat:nqewh7emwzfsbdjhcfeg3ak2qm