SICK: THE SPECTROSCOPIC INFERENCE CRANK
SICK: THE SPECTROSCOPIC INFERENCE CRANK
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SICK：光谱推理曲柄
DOI：
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发表时间：
2016
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通讯作者：
A. Casey
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文献类型：
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作者：
A. Casey
在公共和私人天文档案中存在着大量的光谱数据，这些数据仍然没有得到充分利用。缺乏可靠的开源工具来分析大量的光谱有助于这种情况，随着大型调查连续发布数量级更多的光谱，这种情况将进一步恶化。在这篇文章中，我介绍生病，光谱推断曲柄，一个灵活和快速的贝叶斯工具推断天体物理参数的光谱。Sick对波长覆盖范围、分辨率或一般数据格式是不可知的，允许任何用户容易地为其数据构建生成模型，而不管其来源如何。sick可用于提供模型参数的最近邻估计、数值优化点估计或后验概率分布的全马尔可夫链蒙特卡罗采样。这种普遍性使任何天文学家都能够利用大量已发表的合成和观测光谱，并对许多天体物理（和讨厌的）量进行精确的推断。模型强度可以使用线性多维插值或基于Cannon的模型从现有的合成或观察光谱的网格可靠地近似。变换数据的附加现象（例如，红移、旋转加宽、连续谱、光谱分辨率）作为自由参数被合并，并且可以被边缘化。异常像素（例如，宇宙射线或模型化较差的状态）可以用高斯混合模型来处理，并且包括噪声模型以考虑系统低估的方差。将这些现象结合到标量合理的定量模型中，可以对噪声数据进行精确的推断，并具有可信的不确定性。我描述了常见的模型特性、实现细节和默认行为，这些都是平衡的，适合于大多数天文应用程序。使用低分辨率，高信噪比光谱的M67恒星的前向模型揭示了原子扩散过程的顺序为0.05德克斯，以前只能测量与差分分析技术在高分辨率光谱。sick易于使用，经过良好测试，并在MIT许可下通过GitHub在线免费提供。
There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing 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 is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize 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 marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled 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. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal-to-noise ratio 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. sick is easy to use, well-tested, and freely available online through GitHub under the MIT license.