Application of machine learning in Bose-Einstein condensation critical-temperature analyses of path-integral Monte Carlo simulations

Abstract

We detail the use of simple machine learning algorithms to determine the critical Bose-Einstein condensation (BEC) critical temperature $T_{\text{c}}$ from ensembles of paths created by path-integral Monte Carlo (PIMC) simulations. We quickly overview critical temperature analysis methods from literature, and then compare the results of simple machine learning algorithm analyses with these prior-published methods for one-component Coulomb Bose gases and liquid ${}^4$He, showing good agreement.

7 pages, 5 figures

References in corpus (1)

• Thermal Monopole Condensation and Confinement in finite temperature Yang-Mills Theories