本文介绍了一项利用人工智能技术挖掘X射线源（ULX）数据，以发现新的候选脉冲型ULX（PULX）的研究。研究人员应用无监督聚类算法对XMM-Newton观测的ULX数据库进行分析，根据特定标准将源分为两类，并结合已知PULX的观测数据设定了区分阈值，从而识别出新的候选PULX样本。该样本包含85个独特源，其中大部分有多重观测记录。虽然初步的时域分析尚未发现新的脉冲信号，但这项研究展示了AI方法在天体物理研究中的预测能力，并强调了高统计量观测数据对于验证其有效性的重要性。

🔭 AI技术在天体物理中的应用：该研究利用AI技术，特别是无监督聚类算法，对XMM-Newton任务的高能数据进行分析，旨在从海量数据中识别出具有脉冲信号特征的候选天体，这标志着AI在天体物理数据挖掘领域的重要进展。

💡 发现新的候选脉冲型ULX（PULX）：通过对ULX数据库的深入分析，研究成功识别出一个包含85个独特源的候选PULX样本，其中大部分源拥有多个观测记录。这些候选源的特性在多维相空间中与已知的PULX相似，尽管其光变曲线中尚未直接检测到脉冲信号。

🔬 方法论的创新与挑战：研究方法通过将ULX按相似性聚类，并利用已知PULX数据设定分类阈值，有效缩小了搜寻范围。然而，初步分析未能直接观测到脉冲信号，这说明了高统计量的观测数据对于确认AI预测结果的必要性，也指出了AI方法在实际观测验证中的挑战。

🚀 AI的预测能力与未来展望：这项研究清晰地展示了AI驱动方法在预测新天体和现象方面的潜力。尽管需要进一步的观测数据来验证这些候选PULX的脉冲信号，但AI的应用为未来在数据密集型天体物理研究中发现未知现象提供了有力的工具和方向。

arXiv:2507.15032v1 Announce Type: cross Abstract: 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.