{"paper":{"title":"Recovering Thermodynamics from Spectral Profiles observed by IRIS: A Machine and Deep Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.SR","authors_text":"Alberto Sainz Dalda, Bart De Pontieu, Jaime de la Cruz Rodr\\'iguez, Milan Go\\v{s}i\\'c","submitted_at":"2019-04-17T17:46:02Z","abstract_excerpt":"Inversion codes allow reconstructing a model atmosphere from observations. With the inclusion of optically thick lines that form in the solar chromosphere, such modelling is computationally very expensive because a non-LTE evaluation of the radiation field is required. In this study, we combine the results provided by these traditional methods with machine and deep learning techniques to obtain similar-quality results in an easy-touse, much faster way. We have applied these new methods to Mg II h&k lines observed by IRIS. As a result, we are able to reconstruct the thermodynamic state (tempera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.08390","kind":"arxiv","version":2},"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"}