好的、坏的和丑陋的：SZ检测的统计质量评估
The Good, the Bad and the Ugly: Statistical quality assessment of SZ detections
摘要：
我们研究三种方法的问题，源分类目录。我们的目标是确定这些目录中的元素可以根据其光谱能量分布（SED）在种群中区分的置信度。我们的分析是基于在所考虑的频率范围内的主要信号的综合SED模型上的测量的投影。我们首先考虑似然分析，它介于监督和无监督方法之间。接下来，我们研究一种无监督聚类技术。最后，我们考虑了一个基于人工神经网络的监督分类器。我们说明的方法和结果，使用各种调查的目录。也就是说，X射线（MCXC）、光学（SDSS）和毫米（Planck Sunyaev-Zeldovich（SZ））。我们发现，这三种方法的统计分类的结果是非常好的协议彼此，虽然基于监督神经网络的分类表现出更好的性能，允许最好的分离成人口的可靠和不可靠的来源目录。最新的方法被应用于普朗克卫星探测到的SZ源。它导致了分类评估，从而同意在普朗克SZ目录中公布的可靠性评估。我们的方法可以很容易地应用到未来的大型调查，如SRG/eROSITA和Euclid星表。
英文摘要：
We examine three approaches to the problem of source classification in catalogues. Our goal is to determine the confidence with which the elements in these catalogues can be distinguished in populations on the basis of their spectral energy distribution (SED). Our analysis is based on the projection of the measurements onto a comprehensive SED model of the main signals in the considered range of frequencies. We first first consider likelihood analysis, which half way between supervised and unsupervised methods. Next, we investigate an unsupervised clustering technique. Finally, we consider a supervised classifier based on Artificial Neural Networks. We illustrate the approach and results using catalogues from various surveys. i.e., X-Rays (MCXC), optical (SDSS) and millimetric (Planck Sunyaev-Zeldovich (SZ)). We show that the results from the statistical classifications of the three methods are in very good agreement with each others, although the supervised neural network-based classification shows better performances allowing the best separation into populations of reliable and unreliable sources in catalogues. The latest method was applied to the SZ sources detected by the Planck satellite. It led to a classification assessing and thereby agreeing with the reliability assessment published in the Planck SZ catalogue. Our method could easily be applied to catalogues from future large survey such as SRG/eROSITA and Euclid.
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