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arXiv · 1912.06654

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

Adith Ramamurti
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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.

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BibTeXRIS

Adith Ramamurti. 2019-12-19. Application of machine learning in Bose-Einstein condensation critical-temperature analyses of path-integral Monte Carlo simulations. https://arxiv.org/abs/1912.06654

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