Detecting the phase transition in a strongly interacting Fermi gas by unsupervised machine learning

We study the critical temperature of the superfluid phase transition of strongly interacting fermions in the crossover regime between a Bardeen-Cooper-Schrieffer superconductor and a Bose-Einstein condensate 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.