{"paper":{"title":"Harvesting the Ly\\alpha\\ forest with convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"astro-ph.GA","authors_text":"Gwen Rudie, Ryan Cooke, Ting-Yun Cheng","submitted_at":"2022-09-05T21:02:12Z","abstract_excerpt":"We develop a machine learning based algorithm using a convolutional neural network (CNN) to identify low HI column density Ly$\\alpha$ absorption systems ($\\log{N_{\\mathrm{HI}}}/{\\rm cm}^{-2}<17$) in the Ly$\\alpha$ forest, and predict their physical properties, such as their HI column density ($\\log{N}_{\\mathrm{HI}}/{\\rm cm}^{-2}$), redshift ($z_{\\mathrm{HI}}$), and Doppler width ($b_{\\mathrm{HI}}$). Our CNN models are trained using simulated spectra (S/N $\\simeq10$), and we test their performance on high quality spectra of quasars at redshift $z\\sim2.5-2.9$ observed with the High Resolution Ec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.02142","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/2209.02142/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"}