Machine Learning the Phase Diagram of a Strongly Interacting Fermi Gas

We determine the phase diagram of strongly correlated fermions in the crossover from Bose-Einstein condensates of molecules (BEC) to Cooper pairs of fermions (BCS) utilizing an artificial neural network. By applying advanced image recognition techniques to the momentum distribution of the fermions, a quantity which has been widely considered as featureless for providing information about the condensed state, we measure the critical temperature and show that it exhibits a maximum on the bosonic side of the crossover. Additionally, we backanalyze the trained neural network and demonstrate that it interprets physically relevant quantities.

Automated summary

The phase diagram of strongly correlated fermions in the BEC-BCS crossover is determined using an artificial neural network. By utilizing advanced image recognition techniques on the momentum distribution of the fermions, previously considered as featureless, the critical temperature is measured and shown to exhibit a maximum on the bosonic side of the crossover. Moreover, the trained neural network is analyzed to interpret physically relevant quantities.