{"paper":{"title":"Deep learning denoising by dimension reduction: Application to the ORION-B line cubes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Albrecht Sievers, Annie Hughes, Antoine Roueff (IM2NP), Dariuscz C. Lis, David Languignon, Emeric E. Bron (ICMM), Evelyne Roueff, Fran\\c{c}ois Levrier, Franck Le Petit, Harvey Liszt, Ivana Be\\v{s}li\\'c, Jacques Le Bourlot, Jan H. Orkisz, Javier R Goicoechea, J\\'er\\^ome Pety (IRAM, Jocelyn Chanussot, Jouni Kainulainen, Karin Danielsson \\\"Oberg, LERMA), LERMA (UMR\\_8112)), Lucas Einig (IRAM), Maryvonne Gerin, Miriam Garcia Santa-Maria, Nicolas Peretto, Pascal Tremblin (MDLS), Paul Vandame, Pierre-Antoine Thouvenin (CRIStAL), Pierre Chainais (CRIStAL), Pierre Gratier, Pierre Palud (CRIStAL, Rosine Lallement, S\\'ebastien Bardeau, Victor de Souza Magalhaes, Viviana Guzman Veloso","submitted_at":"2023-07-24T13:02:31Z","abstract_excerpt":"Context. The availability of large bandwidth receivers for millimeter radio telescopes allows the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain much information on the physical, chemical, and kinematical properties of the emitting gas. However, their large size coupled with inhomogenous signal-to-noise ratio (SNR) are major challenges for consistent analysis and interpretation.Aims. We search for a denoising method of the low SNR regions of the studied data cubes that would allow to recover the low SNR emissi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.13009","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/2307.13009/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"}