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We detail the use of simple machine learning algorithms to determine thecritical 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 fromliterature, and then compare the results of simple machine learning algorithmanalyses with these prior-published methods for one-component Coulomb Bosegases and liquid $^4$He, showing good agreement.

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