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Probabilistic Record Linkage in Astronomy: Directional Cross-Identification and Beyond

机译：天文学中的概率记录链接：方向性交叉识别及其他

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摘要

Modern astronomy increasingly relies upon systematic surveys, whose dedicated telescopes continuously observe the sky across varied wavelength ranges of the electromagnetic spectrum; some surveys also observe non-electromagnetic messengers, such as high-energy particles or gravitational waves. Stars and galaxies look different through the eyes of different instruments, and their independent measurements have to be carefully combined to provide a complete, sound picture of the multicolor and eventfuluniverse. The association of an object's independent detections is, however, a difficult problem scientifically, computationally, and statistically, raising varied challenges across diverse astronomical applications. The fundamental problem is finding records in survey databases with directions that match to within the direction uncertainties. Such astronomical versions of the record linkage problem are known by various terms in astronomy: cross-matching; cross-identification; and directional, positional, or spatiotemporal coincidence assessment. Astronomers have developed several statistical approaches for such problems, largely independent of related developments in other disciplines. Here, we review emerging approaches that compute (Bayesian) probabilities for the hypotheses of interest: possible associations or demographic properties of a cosmic population that depend on identifying associations. Many cross-identification tasks can be formulated within a hierarchical Bayesian partition model framework, with components that explicitly account for as-trophysical effects (e.g., source brightness versus wavelength, source motion, or source extent), selection effects, and measurement error. We survey recent developments and highlight important open areas for future research.
机译：现代天文学越来越依赖于系统的测量，其专用望远镜连续不断地观察电磁频谱变化波长范围内的天空。一些调查还观察到非电磁信使，例如高能粒子或重力波。恒星和星系在不同仪器的视线下看起来不同，因此必须仔细组合它们的独立测量值，以提供完整的，完整的多色和多变的宇宙照片。然而，对象的独立检测的关联在科学，计算和统计上都是一个难题，在各种天文应用中提出了各种挑战。基本问题是在调查数据库中查找方向与方向不确定性相匹配的记录。记录链接问题的这种天文学形式在天文学中用各种术语众所周知：交叉匹配;交叉识别;以及方向性，位置性或时空一致性评估。天文学家已经针对此类问题开发了几种统计方法，这些方法在很大程度上与其他学科的相关发展无关。在这里，我们回顾新兴的方法来计算感兴趣假设的（贝叶斯）概率：依赖于识别关联的宇宙人口的可能关联或人口统计特性。可以在分层贝叶斯分区模型框架内制定许多交叉标识任务，其中的组件明确说明了天体物理效应（例如光源亮度与波长，光源运动或光源范围），选择效果和测量误差。我们调查了最近的发展，并突出了重要的开放领域，以供将来研究。

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