标题： 病态，光谱推断机构 显示英文标题

标题： Sick, the spectroscopic inference crank

摘要： 存在大量公共和私人天文学档案中的光谱数据，这些数据被严重未充分利用。 缺乏可靠的开源工具来分析大量光谱数据加剧了这种情况，随着大型调查依次释放数量级更多的光谱数据，这种情况预计会恶化。 在本文中，我介绍了sick，即光谱推断曲柄，一种灵活且快速的贝叶斯工具，用于从光谱中推断天体物理参数。 sick可用于提供模型参数的最近邻估计、数值优化的点估计，或后验概率分布的完整马尔可夫链蒙特卡洛采样。 这种通用性使任何天文学家都能利用大量已发表的合成和观测光谱，并对一系列天体物理（和干扰）量做出精确推断。 可以通过线性多维插值或基于Cannon的模型，从现有的合成或观测光谱网格中可靠地近似模型强度。 将数据转换的附加现象（例如，红移、旋转展宽、连续谱、光谱分辨率）作为自由参数引入，并可以进行边缘化处理。 异常像素（例如，宇宙射线或建模不佳的区域）可以用高斯混合模型处理，并包含一个噪声模型以解释系统性低估的方差。 将这些现象结合到一个标量合理、定量的模型中，可以在嘈杂的数据上进行精确推断并获得可信的不确定性。 使用低分辨率、高信噪比的M67恒星光谱的正向模型揭示了原子扩散过程，其程度约为0.05 dex，以前只能通过高分辨率光谱中的差异分析技术测量。 [简写] 显示英文摘要

摘要： There exists an inordinate amount of spectral data in both public and private astronomical archives which remain severely under-utilised. The lack of reliable open-source tools for analysing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this Article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick can be used to provide a nearest-neighbour estimate of model parameters, a numerically optimised point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalise on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalised away. Outlier pixels (e.g., cosmic rays or poorly modelled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. Using a forward model on low-resolution, high S/N spectra of M67 stars reveals atomic diffusion processes on the order of 0.05 dex, previously only measurable with differential analysis techniques in high-resolution spectra. [abridged]