脉冲星候选选择五十年：从简单的滤波器到新的原则性实时分类方法
Fifty Years of Pulsar Candidate Selection: From simple filters to a new principled real-time classification approach
摘要：
测量规格的改进导致脉冲星候选数量和数据量呈指数级增长。我们研究的候选过滤器，以减轻这些问题在过去的五十年。我们发现，一些现有的方法，如应用限制的候选人收集的总人数，每次观察，可能会对脉冲星搜索的成功产生不利影响。这些方法免疫这种影响被认为是装备不良，以处理与增加数据量和候选人数量，激励新的方法的发展相关的问题。因此，我们提出了一种新的方法设计的在线操作。它使用专门构建的基于树的机器学习分类器，高斯Hellinger非常快速决策树（GH-VFDT）和一组新的用于描述候选人的特征来选择有希望的候选人。这些特征的选择是为了i）最大限度地分离噪声产生的候选物和可能的天体物理起源，ii）尽可能独立于测量。使用这些功能，我们的新方法可以在几秒钟内处理数百万个候选人（每15秒约100万），具有高水平的脉冲星召回率（90%以上）。因此，这一技术适用于平方公里阵列（SKA）预计将产生的大量数据。使用这种方法已协助在LOFAR捆绑阵列全天巡天（LOTAAS）期间获得的数据中发现了20颗新的双星。
英文摘要：
Improving survey specifications are causing an exponential rise in pulsar candidate numbers and data volumes. We study the candidate filters used to mitigate these problems during the past fifty years. We find that some existing methods such as applying constraints on the total number of candidates collected per observation, may have detrimental effects on the success of pulsar searches. Those methods immune to such effects are found to be ill-equipped to deal with the problems associated with increasing data volumes and candidate numbers, motivating the development of new approaches. We therefore present a new method designed for on-line operation. It selects promising candidates using a purpose-built tree-based machine learning classifier, the Gaussian Hellinger Very Fast Decision Tree (GH-VFDT), and a new set of features for describing candidates. The features have been chosen so as to i) maximise the separation between candidates arising from noise and those of probable astrophysical origin, and ii) be as survey-independent as possible. Using these features our new approach can process millions of candidates in seconds (~1 million every 15 seconds), with high levels of pulsar recall (90%+). This technique is therefore applicable to the large volumes of data expected to be produced by the Square Kilometre Array (SKA). Use of this approach has assisted in the discovery of 20 new pulsars in data obtained during the LOFAR Tied-Array All-Sky Survey (LOTAAS).
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