{"paper":{"title":"Physical Parameterization of Stellar Spectra: The Neural Network Approach","license":"","headline":"","cross_cats":[],"primary_cat":"astro-ph","authors_text":"(2) Royal Greenwich Observatory, (3) University of Wisconsin, Cambridge, Coryn A. L. Bailer-Jones (1), Gerard Gilmore (1), Madison), Mike Irwin (2), Ted von Hippel (3) ((1) Institute of Astronomy","submitted_at":"1997-08-22T10:39:32Z","abstract_excerpt":"We present a technique which employs artificial neural networks to produce physical parameters for stellar spectra. A neural network is trained on a set of synthetic optical stellar spectra to give physical parameters (e.g. T_eff, log g, [M/H]). The network is then used to produce physical parameters for real, observed spectra.\n  Our neural networks are trained on a set of 155 synthetic spectra, generated using the SPECTRUM program written by Gray (Gray & Corbally 1994, Gray & Arlt 1996). Once trained, the neural network is used to yield T_eff for over 5000 B-K spectra extracted from a set of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"astro-ph/9708206","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"}