Detecting the phase transition in a strongly-interacting Fermi gas by unsupervised machine learning
arXiv:2310.15989 · doi:10.1103/PhysRevA.108.063303
Abstract
We study the critical temperature of the superfluid phase transition of strongly-interacting fermions in the crossover regime between a Bardeen-Cooper-Schrieffer (BCS) superconductor and a Bose-Einstein condensate (BEC) of dimers. To this end, we employ the technique of unsupervised machine learning using an autoencoder neural network which we directly apply to time-of-flight images of the fermions. We extract the critical temperature of the phase transition from trend changes in the data distribution revealed in the latent space of the autoencoder bottleneck.
arXiv commentPRA, in press
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