标题： 正样本和未标记机器学习揭示新的快速射电暴重复源候选者 显示英文标题

标题： Positive and unlabelled machine learning reveals new fast radio burst repeater candidates

摘要： 快速射电暴（FRBs）是起源未知的天文学射电瞬变现象。 少数FRBs已被观测到起源于重复源，而尚不清楚哪些看似一次性爆发实际上是隐藏的重复源。 最近的研究越来越多地表明，重复和非重复FRBs之间存在固有的物理差异。 之前的研究使用了机器学习分类技术来识别具有重复特征的看似非重复源，这些源的天空位置将是未来观测活动的理想目标。 然而，这些方法未能充分考虑数据的正例和未标记（PU）性质，其中只有重复源的真实标签可用。 不会无意中学到隐藏重复源属性作为非重复源特征的修改技术，可能会更准确地识别出更多的重复源候选者。 本文首次尝试应用专门针对PU的数据机器学习技术来研究FRBs。 我们在现有数据上训练了一个由五个专门针对PU的分类器组成的集成模型，并利用它们在CHIME/FRB合作的爆发数据中识别出66个重复源候选者，其中18个在以往的研究中未被机器学习分类器识别出来。 我们的结果还支持重复源和非重复源具有固有的不同物理特性，特别是频谱指数、频率宽度和爆发宽度。 这项工作还为使用PU学习框架研究重复和非重复FRBs开辟了新的可能性。 显示英文摘要

摘要： Fast radio bursts (FRBs) are astronomical radio transients of unknown origin. A minority of FRBs have been observed to originate from repeating sources, and it is unknown which apparent one-off bursts are hidden repeaters. Recent studies increasingly suggest that there are intrinsic physical differences between repeating and non-repeating FRBs. Previous research has used machine learning classification techniques to identify apparent non-repeaters with repeater characteristics, whose sky positions would be ideal targets for future observation campaigns. However, these methods have not sufficiently accounted for the positive and unlabelled (PU) nature of the data, wherein true labels are only available for repeaters. Modified techniques that do not inadvertently learn properties of hidden repeaters as characteristic of non-repeaters are likely to identify additional repeater candidates with greater accuracy. We present in this paper the first known attempt at applying PU-specific machine learning techniques to study FRBs. We train an ensemble of five PU-specific classifiers on the available data and use them to identify 66 repeater candidates in burst data from the CHIME/FRB collaboration, 18 of which were not identified with the use of machine learning classifiers in past research. Our results additionally support repeaters and non-repeaters having intrinsically different physical properties, particularly spectral index, frequency width, and burst width. This work additionally opens new possibilities to study repeating and non-repeating FRBs using the framework of PU learning.