{"paper":{"title":"Reconstructing the Stripping History of the Sagittarius Stream with Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A neural network trained on simulations infers when Sagittarius Stream stars were stripped from their dwarf galaxy, revealing a clear metallicity gradient with time.","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"Cuihua Du, Haoyang Liu, Jian Zhang, Mingji Deng, Zhongcheng Li","submitted_at":"2026-05-14T03:21:24Z","abstract_excerpt":"The Sagittarius (Sgr) Stream is produced by the ongoing disruption of the Sgr dwarf spheroidal (dSph) galaxy and is thought to contain multiple wraps that were stripped during different pericentric passages. In this study, we introduce a neural-network--based method trained on $N$-body simulations to infer the stripping time of Sgr Stream stars directly from their phase-space coordinates. We combine spectroscopic data from SEGUE, APOGEE DR17, and LAMOST DR7 LRS with \\textit{Gaia} EDR3 astrometry and distance estimates from the latest \\texttt{StarHorse} catalog to identify high-quality Sgr Stre"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Applying our method to these stars, we measure a clear metallicity gradient with stripping time, well described by a linear relation with slope ∼0.3 dex Gyr^{-1}. We further predict the stripping times of globular clusters previously suggested to originate from the Sgr dSph.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The neural network trained on N-body simulations accurately generalizes to real observational data without significant biases arising from simulation assumptions, data selection, or observational uncertainties.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A neural network trained on simulations infers stripping times for Sagittarius stream stars from phase-space data, measuring a 0.3 dex/Gyr metallicity gradient and estimating ages for globular clusters such as Pal 12 and NGC 2419.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A neural network trained on simulations infers when Sagittarius Stream stars were stripped from their dwarf galaxy, revealing a clear metallicity gradient with time.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"0248081a1dbb269d496a9519ab7f8f995b92e9fa3e0c79b374bb95786e453efd"},"source":{"id":"2605.14308","kind":"arxiv","version":1},"verdict":{"id":"b47d053b-7529-4d5e-b5a4-004c911bba60","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T02:36:26.654522Z","strongest_claim":"Applying our method to these stars, we measure a clear metallicity gradient with stripping time, well described by a linear relation with slope ∼0.3 dex Gyr^{-1}. We further predict the stripping times of globular clusters previously suggested to originate from the Sgr dSph.","one_line_summary":"A neural network trained on simulations infers stripping times for Sagittarius stream stars from phase-space data, measuring a 0.3 dex/Gyr metallicity gradient and estimating ages for globular clusters such as Pal 12 and NGC 2419.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The neural network trained on N-body simulations accurately generalizes to real observational data without significant biases arising from simulation assumptions, data selection, or observational uncertainties.","pith_extraction_headline":"A neural network trained on simulations infers when Sagittarius Stream stars were stripped from their dwarf galaxy, revealing a clear metallicity gradient with time."},"references":{"count":111,"sample":[{"doi":"10.3847/1538-4357/ac7c74","year":null,"title":"The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package","work_id":"9813b49d-cd2b-4c8c-a781-07e55454f120","ref_index":1,"cited_arxiv_id":"2206.14220","is_internal_anchor":true},{"doi":"10.3847/1538-3881/aabc4f","year":null,"title":"The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package","work_id":"88a88437-2e0a-4987-83f9-ce7a5d386437","ref_index":2,"cited_arxiv_id":"1801.02634","is_internal_anchor":true},{"doi":"10.1051/0004-6361/201322068","year":2013,"title":"arXiv , Author =:1307.6212 , Journal =","work_id":"02354d9b-ad6f-4a4e-9720-06d21c2feba5","ref_index":3,"cited_arxiv_id":"1307.6212","is_internal_anchor":true},{"doi":"10.1093/mnras/183.3.341","year":null,"title":"9Ã uVBʧX˛=ұ K Td GjZoUU Ԫ[E>-uhc 7ch HεVS :SQ0<( ? 6v","work_id":"5b99fb29-3881-48a2-87e3-925aa0ee5e6b","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1038/nature03597","year":null,"title":"Simulating the joint evolution of quasars, galaxies and their large-scale distribution","work_id":"d7c52e22-1d67-45cc-9512-2834a57ec6ad","ref_index":5,"cited_arxiv_id":"astro-ph/0504097","is_internal_anchor":true}],"resolved_work":111,"snapshot_sha256":"c0d9aced5d755ec080ddcf58b21f3daa149f0c9112ffe83222d234766d115e01","internal_anchors":60},"formal_canon":{"evidence_count":2,"snapshot_sha256":"2fef02770529c1a1277218c3a9461b1256e374e7669c86c0aed9a4bedacd495f"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}