Machine Learning the Phase Diagram of a Strongly Interacting Fermi Gas
M. Link, K. Gao, A. Kell, Moritz Breyer, D. Eberz, Benjamin Rauf, Michael Köhl
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 backanalyze the trained neural network and demonstrate that it interprets physically relevant quantities.
Topics & Concepts
FermionPhysicsPhase diagramCrossoverMomentum (technical analysis)Artificial neural networkMeasure (data warehouse)Phase (matter)Statistical physicsCondensed matter physicsQuantum mechanicsArtificial intelligenceComputer scienceData miningEconomicsFinanceCold Atom Physics and Bose-Einstein CondensatesQuantum, superfluid, helium dynamicsQuantum many-body systems