用AI从海量数据中筛选出85个疑似脉动的超亮X射线源候选者。
The hunt for new pulsating ultraluminous X-ray sources: a clustering approach
- 基于无监督聚类，从多维特征中识别与已知脉动源相似的候选者。
- 发现85个新候选者，其中约85%有多个观测记录。
- 虽暂未发现新脉动信号，但为后续高统计观测提供目标列表。
在少数超亮X射线源（ULXs）中发现快速变化的相干信号，证实了超爱丁顿吸积中子星的存在，彻底改变了对ULX类别的理解。当前探测脉动的能力受限于统计数据不足。然而，高能天文任务的目录和档案中蕴含可用于识别新脉动超亮X射线源（PULX）候选者的信息。本研究旨在从尚未显示脉动的ULX中筛选出可能具有脉动特性的候选者。我们对XMM-Newton探测到的更新版ULX数据库应用了一种人工智能方法：首先使用无监督聚类算法将源按特征分为两类；然后利用已知PULX观测样本确定两类间的分离阈值，并识别出包含新候选者的簇。结果表明，仅需少数几个判据即可判定观测归属。新候选簇包含85个独立源、共355次观测，其中约85%的候选者有多次观测。初步时序分析未发现新脉动信号。该工作提出了一组由XMM-Newton观测的新型候选PULX，其多维特征空间表现与已知PULX相似，尽管光变曲线中尚未出现脉动。这一成果展示了基于AI方法的预测能力，同时也凸显了需要更高统计量的观测数据来揭示这些源中的相干信号，以验证该方法的可靠性。
原文摘要 · Abstract (English)
The discovery of fast and variable coherent signals in a handful of ultraluminous X-ray sources (ULXs) testifies to the presence of super-Eddington accreting neutron stars, and drastically changed the understanding of the ULX class. Our capability of discovering pulsations in ULXs is limited, among others, by poor statistics. However, catalogues and archives of high-energy missions contain information which can be used to identify new candidate pulsating ULXs (PULXs). The goal of this research is to single out candidate PULXs among those ULXs which have not shown pulsations due to an unfavourable combination of factors. We applied an AI approach to an updated database of ULXs detected by XMM-Newton. We first used an unsupervised clustering algorithm to sort out sources with similar characteristics into two clusters. Then, the sample of known PULX observations has been used to set the separation threshold between the two clusters and to identify the one containing the new candidate PULXs. We found that only a few criteria are needed to assign the membership of an observation to one of the two clusters. The cluster of new candidate PULXs counts 85 unique sources for 355 observations, with $\sim$85% of these new candidates having multiple observations. A preliminary timing analysis found no new pulsations for these candidates. This work presents a sample of new candidate PULXs observed by XMM-Newton, the properties of which are similar (in a multi-dimensional phase space) to those of the known PULXs, despite the absence of pulsations in their light curves. While this result is a clear example of the predictive power of AI-based methods, it also highlights the need for high-statistics observational data to reveal coherent signals from the sources in this sample and thus validate the robustness of the approach.
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