{"paper":{"title":"A Machine-Learning Compositional Study of Exoplanetary Material Accreted Onto Five Helium-Atmosphere White Dwarfs with $\\texttt{cecilia}$","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","astro-ph.SR","cs.LG"],"primary_cat":"astro-ph.EP","authors_text":"Amy Bonsor, Andrew Vanderburg, Ben Zuckerman, Javier Via\\~na, Kishalay De, Laura K. Rogers, Mariona Badenas-Agusti, Patrick Dufour, Simon Blouin, Siyi Xu, Susana Hoyos","submitted_at":"2025-05-09T17:59:50Z","abstract_excerpt":"We present the first application of the Machine Learning (ML) pipeline $\\texttt{cecilia}$ to determine the physical parameters and photospheric composition of five metal-polluted He-atmosphere white dwarfs without well-characterised elemental abundances. To achieve this, we perform a joint and iterative Bayesian fit to their $\\textit{SDSS}$ (R=2,000) and $\\textit{Keck/ESI}$ (R=4,500) optical spectra, covering the wavelength range from about 3,800\\r{A} to 9,000\\r{A}. Our analysis measures the abundances of at least two $-$and up to six$-$ chemical elements in their atmospheres with a predictive"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.06228","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/2505.06228/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"}