{"paper":{"title":"Supercool with PPO: Exploring Supercooled Phase Transitions via Reinforcement Learning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Wan-Zhe Feng, Zi-Hui Zhang, Zong-Huan Ye","submitted_at":"2026-06-24T18:00:33Z","abstract_excerpt":"Gravitational waves from cosmological first-order phase transitions provide a powerful probe of hidden sectors and beyond the Standard Model physics. However, identifying phenomenologically relevant benchmark points remains computationally challenging, since viable and detectable signals typically occupy only a small fraction of the scanned parameter space. In this work, we introduce a reinforcement learning strategy based on Proximal Policy Optimization (PPO) to accelerate the search for gravitational wave signals from supercooled phase transitions in a minimal dark $U(1)_x$ sector. We constr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26251","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.26251/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"}