发表机构
University of Oklahoma; INAF - Ossevatorio Astronomico di Padova（俄克拉荷马大学; 意大利国家天体物理研究所帕多瓦天文台）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于机器学习的流程，直接拟合未归一化SED，为320万颗DESI恒星提供参数及12种元素丰度，精度优于官方SP流程。
AI 中文摘要
我们提出了一种机器学习流程，用于从暗能量光谱仪（DESI）观测的光谱中确定恒星参数和化学丰度。在模型驱动的Payne框架基础上，我们训练了一个人工神经网络，使用了约37万条覆盖FGK型恒星的合成光谱，并将网络直接拟合到每颗恒星的流量定标、未归一化的光谱能量分布（SED）上，而非像DESI官方恒星参数（SP）流程那样使用连续谱归一化光谱。内部精度测试显示，在合成验证样本的90%中，中位插值误差小于0.3%，且完整的拟合流程即使在低信噪比（S/N）下也能高精度地恢复输入标签。我们在交叉匹配的6719颗APOGEE和3455颗GALAH恒星样本上验证了我们的方法，这些恒星由DESI观测且信噪比大于20，我们恢复的Teff和logg的系统性偏差小于DESI SP流程，我们将这一改进归因于拟合完整的SED形状而非归一化光谱。我们还恢复了12种元素丰度（Na、Mg、Al、Si、Ca、Ti、V、Cr、Mn、Ni、Ba和Y），其精度在可比较的情况下达到或超过SP，并提供了SP完全不报告的四种丰度：Mn、V、Ba和Y。将该流程应用于DESI数据发布1的恒星样本，我们交付了包含320万颗恒星的恒星参数和丰度目录。
英文摘要
We present a machine-learning pipeline for determining stellar parameters and chemical abundances from spectra observed by the Dark Energy Spectroscopic Instrument (DESI). Building on the model-driven Payne framework, we train an artificial neural network on ~370,000 synthetic spectra spanning FGK stars, and fit the network directly to the flux-calibrated, un-normalised spectral energy distribution (SED) of each star, rather than to a continuum-normalised spectrum as done by DESI's official Stellar Parameter (SP) pipeline. Internal accuracy tests show a median interpolation error of less than 0.3% for 90% of a synthetic verification sample, and the full fitting workflow recovers input labels to high accuracy even at low signal-to-noise ratio (S/N). Validating our method on cross-matched samples of 6719 APOGEE and 3455 GALAH stars observed by DESI with S/N > 20, we recover Teff and logg with smaller systematic offsets than the DESI SP pipeline, an improvement we attribute to fitting the full SED shape rather than a normalised spectrum. We further recover 12 elemental abundances (Na, Mg, Al, Si, Ca, Ti, V, Cr, Mn, Ni, Ba, and Y) to an accuracy that matches or exceeds SP where a comparison is possible, and provide four abundances, Mn, V, Ba, and Y, that SP does not report at all. Applying this pipeline to the DESI Data Release 1 stellar sample, we deliver a catalogue of stellar parameters and abundances for 3.2 million stars.
Comments16 pages, 8 figures, Submitted to ApJS
