Machine-learning the phase diagram of a strongly-interacting Fermi gas
Quantum Gases
2023-10-25 v1
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.
Keywords
Cite
@article{arxiv.2310.16006,
title = {Machine-learning the phase diagram of a strongly-interacting Fermi gas},
author = {M. Link and K. Gao and A. Kell and M. Breyer and D. Eberz and B. Rauf and M. Köhl},
journal= {arXiv preprint arXiv:2310.16006},
year = {2023}
}