Machine-learning the phase diagram of a strongly-interacting Fermi gas

M. Link; K. Gao; A. Kell; M. Breyer; D. Eberz; B. Rauf; M. Köhl

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arXiv · 2310.16006

Machine-learning the phase diagram of a strongly-interacting Fermi gas

M. Link·K. Gao·A. Kell·M. Breyer·D. Eberz·B. Rauf·M. Köhl

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Abstract

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 back-analyze the trained neural network and demonstrate that it interprets physically relevant quantities.

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M. Link, K. Gao, A. Kell, M. Breyer, D. Eberz, B. Rauf, M. Köhl. 2023-10-24. Machine-learning the phase diagram of a strongly-interacting Fermi gas. https://arxiv.org/abs/2310.16006

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