{
  "id": "1912.06654",
  "version": "v1",
  "published": "2019-12-14T17:25:51.000Z",
  "updated": "2019-12-14T17:25:51.000Z",
  "title": "Application of machine learning in Bose-Einstein condensation critical-temperature analyses of path-integral Monte Carlo simulations",
  "authors": [
    "Adith Ramamurti"
  ],
  "comment": "6 pages, 5 figures",
  "categories": [
    "cond-mat.stat-mech",
    "cond-mat.quant-gas",
    "physics.comp-ph"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-12-14T17:25:51.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "bose-einstein condensation critical-temperature analyses",
      "path-integral monte carlo simulations",
      "critical temperature analysis methods",
      "overview critical temperature analysis",
      "simple machine learning algorithm"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 6,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}