发表机构
Vanderbilt University; Canadian Institute for Theoretical Astrophysics, University of Toronto; Monash University; Center for Computational Astrophysics, Flatiron Institute; University of Chicago; Kavli Institute for Cosmological Physics, University of Chicago; NSF-Simons AI Institute for the Sky (SkAI); Texas Christian University; Georgia State University; Observatories of the Carnegie Institution for Science（范德堡大学; 多伦多大学加拿大理论天体物理研究所; 蒙纳士大学; 平顿研究所计算天体物理学中心; 芝加哥大学; 芝加哥大学卡弗里宇宙物理学研究所; NSF-西蒙斯天空人工智能研究所; 基督教徒大学; 佐治亚州立大学; 卡内基科学机构天文台）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用BOSS-CLAM管道从BOSS光谱推断恒星参数，通过多项式映射与光谱分解联合优化，标签来自多源，能覆盖多种恒星，推断大量光谱参数，验证表明其在丰度推断上表现良好，适合银河系相关研究且已公开发布。
AI 中文摘要
大型光谱巡天需要强大的管道，能从不同质量的数据中推断赫罗图上广泛范围内的恒星参数。SDSS-V就是这样一项巡天，其中低分辨率光学BOSS光谱仪的数据将提供涵盖广泛银河系恒星群体的大型数据集。为更好分析这些数据，我们提出BOSS-CLAM，这是一种用于从连续归一化BOSS光谱推断有效温度（$T_\mathrm{eff}$）、表面重力（$\log g$）、金属丰度（$[\mathrm{Fe/H}]$）和$\alpha$丰度（$[\alpha/\mathrm{M}]$）的生成式正向建模管道。BOSS-CLAM通过与光谱分解联合优化的多项式映射将恒星标签映射到非负矩阵分解（NMF）基向量权重，为处理低分辨率BOSS数据提供更灵活框架。此外，训练标签来自四个互补源，能覆盖从冷M矮星到热OB星以及广泛的金属丰度范围。我们推断了1,708,214条BOSS光谱的参数，推荐了915,514个源的干净目录。对开放和球状星团的验证表明在广泛金属丰度范围内丰度均匀且准确。宽双星测试在信噪比为10时产生的丰度不确定性为$\sigma_{[\mathrm{Fe/H}]} \approx 0.15$ dex和$\sigma_{[\alpha/\mathrm{M}]} \approx 0.06$ dex。最后，我们证明BOSS-CLAM目录恢复了银河系盘已知的化学结构，非常适合银河系考古、化学标记和恒星群体建模。该管道、训练模型和目录作为SDSS-V DR20的一部分公开发布。
英文摘要
Large spectroscopic surveys require robust pipelines capable of inferring stellar parameters over a wide range of the Hertzsprung-Russell (HR) diagram from data of varying quality. SDSS-V is one such survey, where the data from the lower-resolution, optical BOSS spectrograph will provide a large dataset covering a wide range of Galactic stellar populations. To better analyze these data, we present BOSS-CLAM, a generative, forward modeling pipeline for inferring effective temperature ($T_\mathrm{eff}$), surface gravity ($\log g$), metallicity ($[\mathrm{Fe/H}]$), and $α-$abundance ($[α/\mathrm{M}]$) from continuum-normalized BOSS spectra. BOSS-CLAM maps stellar labels to Non-negative Matrix Factorization (NMF) basis vector weights via a polynomial mapping jointly optimized with the spectral decomposition, which provides a more flexible framework for working with the lower-resolution BOSS data. Additionally, training labels are drawn from four complementary sources (ASPCAP, BOSS-MINESweeper, wide binaries, and a hot star validation sample), which enables coverage from cool M dwarfs through hot OB stars, and across a wide range of metallicity. We infer parameters for 1,708,214 BOSS spectra, with a recommended clean catalog of 915,514 sources. Validation against open and globular clusters demonstrates homogeneous, accurate abundances across a wide range of metallicity. Wide binary tests yield abundance uncertainties of $σ_{[\mathrm{Fe/H}]} \approx 0.15$ dex and $σ_{[α/\mathrm{M}]} \approx 0.06$ dex at SNR = 10. Finally, we demonstrate that the BOSS-CLAM catalog recovers known chemical structure of the Milky Way disk and is well-suited for Galactic archaeology, chemical tagging, and stellar population modeling. The pipeline, trained model, and catalog are publicly released as part of SDSS-V DR20.
Comments32 pages, 19 figures
