Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks

Abstract

The detection of the effects of spin symmetry in momentum distribution of an SU(N)-symmetric Fermi gas has remained challenging. Here, the authors use supervised machine learning to connect the spin multiplicity to thermodynamic quantities associated with different parts of the momentum distribution.