{"paper":{"title":"Optimizing Quantum Gas Production by an Evolutionary Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.atom-ph"],"primary_cat":"cond-mat.quant-gas","authors_text":"Artur Widera, Daniel Mayer, Farina Kindermann, Felix Schmidt, Michael Hohmann, Tobias Lausch","submitted_at":"2016-01-22T15:17:29Z","abstract_excerpt":"We report on the application of an evolutionary algorithm (EA) to enhance performance of an ultra-cold quantum gas experiment. The production of a $^{87}$Rubidium Bose-Einstein condensate (BEC) can be divided into fundamental cooling steps, specifically magneto optical trapping of cold atoms, loading of atoms to a far detuned crossed dipole trap and finally the process of evaporative cooling. The EA is applied separately for each of these steps with a particular definition for the feedback the so-called fitness. We discuss the principles of an EA and implement an enhancement called differentia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.06038","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}