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

ABSTRACT: The power of machine learning (ML) provides the possibility of analyzing experimental measurements with a high sensitivity. However, it still remains challenging to probe the subtle effects directly related to physical observables and to understand physics behind from ordinary experimental data using ML. Here, we introduce a heuristic machinery by using machine learning analysis. We use our machinery to guide the thermodynamic studies in the density profile of ultracold fermions interacting within SU(N) spin symmetry prepared in a quantum simulator. Although such spin symmetry should manifest itself in a many-body wavefunction, it is elusive how the momentum distribution of fermions, the most ordinary measurement, reveals the effect of spin symmetry. Using a fully trained convolutional neu

SUBMITTER: Zhao E 

PROVIDER: S-EPMC8012572 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature