天文源多波长关联的新方法
A New Approach to Multi-wavelength Associations of Astronomical Sources
摘要：
当前和下一代天文测量面临的最大问题之一是试图在不同的数据质量和位置不确定性的波长范围内产生大量准确的交叉识别。直到最近，简单的空间“最近邻”关联对于大多数应用已经足够了。然而，随着仪器的进步，可以制作更敏感的图像，源密度的快速增加意味着跨多个波长的源混淆是一个严重的问题。远红外和亚毫米天文学领域尤其受到这些问题的阻碍。当前亚毫米和远红外仪器的差的角分辨率使得在许多情况下对于其它波长的每个源存在多个合理的对应物。在这里，我们提出了一种新的自动化方法，使用Budavari和Szalay（2008）提出的贝叶斯框架中的空间和SED信息集的组合，在不同波长的源之间产生关联。该技术的测试进行模拟目录的源从GaLICS和真实数据从多波长观测的SXDF。结果发现，一个单一的品质因数，贝叶斯因子，可以有效地用来描述在匹配的信心。这种技术的进一步应用，以未来的赫歇尔数据集进行了讨论。
英文摘要：
One of the biggest problems faced by current and next-generation astronomical surveys is trying to produce large numbers of accurate cross identifications across a range of wavelength regimes with varying data quality and positional uncertainty. Until recently simple spatial "nearest neighbour" associations have been sufficient for most applications. However as advances in instrumentation allow more sensitive images to be made the rapid increase in the source density has meant that source confusion across multiple wavelengths is a serious problem. The field of far-IR and sub-mm astronomy has been particularly hampered by such problems. The poor angular resolution of current sub-mm and far-IR instruments is such that in a lot of cases there are multiple plausible counterparts for each source at other wavelengths. Here we present a new automated method of producing associations between sources at different wavelengths using a combination of spatial and SED information set in the Bayesian framework presented by Budavari & Szalay (2008). Testing of the technique is performed on both simulated catalogues of sources from GaLICS and real data from multi-wavelength observations of the SXDF. It is found that a single figure of merit, the Bayes factor, can be effectively used to describe the confidence in the match. Further applications of this technique to future Herschel datasets are discussed.
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