학술논문

Machine-learning the phase diagram of a strongly-interacting Fermi gas
Document Type
Working Paper
Source
Phys. Rev. Lett. 130, 203401 (2023)
Subject
Condensed Matter - Quantum Gases
Language
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.