Upcoming million-star spectroscopic surveys have the potential to revolutionize our view of the formation and chemical evolution of the Milky Way. Realizing this potential requires automated approaches to optimize estimates of stellar properties, such as chemical element abundances, from the spectra. The volume and quality of the observations strongly motivate that these approaches should be data-driven. With this in mind, we introduce SSSpaNG: a data-driven Gaussian Process model of stellar spectra. We demonstrate the capabilities of SSSpaNG using a sample of APOGEE red clump stars, whose model parameters we infer via Gibbs sampling. Pooling information between stars to infer their covariance, we permit clear identification of the correlations between spectral pixels. Harnessing these correlations, we infer the true spectrum of each star, inpainting missing regions and denoising by a factor of at least 2-3 for low-signal-to-noise stars. As we marginalize over the covariance matrix of the spectra, the effective prior on these true spectra is non-Gaussian, non-convex and sparsifying, promoting typically small but occasionally large excursions from the mean. The high-fidelity true spectra produced will enable improved elemental abundance measurements for individual stars. Our model also allows us to quantify the information gained by observing portions of a star's spectrum, and thereby define the most mutually informative spectral regions. Using 25 windows centred on elemental absorption lines, we demonstrate that the iron-peak and alpha-process elements are particularly mutually informative for these spectra, and that the majority of information about a target window is contained in the 10-or-so most informative windows. Our information-gain metric has the potential to inform models of nucleosynthetic yields and optimize the design of future observations.

中文翻译：

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斯潘！恒星光谱作为稀疏的、数据驱动的、非高斯过程

即将进行的百万星光谱调查有可能彻底改变我们对银河系形成和化学演化的看法。实现这一潜力需要自动化方法来优化从光谱中估计恒星属性，例如化学元素丰度。观察的数量和质量强烈促使这些方法应该是数据驱动的。考虑到这一点，我们介绍了 SSSpaNG：一种数据驱动的恒星光谱高斯过程模型。我们使用 APOGEE 红团星样本展示了 SSSpaNG 的功能，我们通过 Gibbs 采样推断其模型参数。汇集恒星之间的信息以推断它们的协方差，我们可以清楚地识别光谱像素之间的相关性。利用这些相关性，我们推断出每颗恒星的真实光谱，对于低信噪比的恒星，修复缺失区域并降噪至少 2-3 倍。当我们边缘化光谱的协方差矩阵时，这些真实光谱的有效先验是非高斯、非凸和稀疏的，通常会促进平均值的小但偶尔大的偏移。产生的高保真真实光谱将能够改进单个恒星的元素丰度测量。我们的模型还允许我们量化通过观察恒星光谱部分获得的信息，从而定义最能相互提供信息的光谱区域。使用以元素吸收线为中心的 25 个窗口，我们证明了铁峰元素和 α 过程元素对于这些光谱特别相互提供信息，并且关于目标窗口的大部分信息都包含在 10 个左右的信息量最大的窗口中。我们的信息增益指标有可能为核合成产量模型提供信息并优化未来观察的设计。