切达档案中的X射线暂现现象：引入累积分布判别器（CuDiDi）
X-ray transients in the Chandra archive: Introducing the cumulative distribution discriminator (CuDiDi)
AI总结：
本文通过新算法在切达档案中发现765个高可信度X射线暂现现象，涵盖多种时标和光度，扩展了Chandra暂现体样本，建立了利用存档数据发现罕见高能现象的方法。
摘要：
子观测时间尺度上的X射线暂现现象代表了一类多样且未被充分研究的天体物理现象，从恒星耀斑、磁星爆发到 extragalactic 快暂现现象和超新星冲击波爆发。我们通过一种结合源识别、光变曲线分析、目录交叉匹配和新统计分类器累积分布判别器（CuDiDi）的系统搜索方法，在20,212个切达ACIS观测中寻找此类事件。从1,420个初始候选者中，我们确定了765个高可信度的暂现体样本，涵盖广泛的时间尺度、流量和光谱形状。候选者分布在整个天空上，显示了广泛的时间持续时间，中位数为10 ks。一部分持续时间<30秒的快速事件显示出非常软的光谱，可能由耀斑矮星引起，但不能排除extragalactic现象。与之前发表的样本相比，CuDiDi识别了大多数已知的暂现体，同时施加了更严格的变异性标准，并将Chandra暂现体样本扩展以包括更短的事件。我们提供了全面的子观测切达X射线暂现体目录，并建立了利用存档数据发现罕见短寿命高能现象的一般方法。
英文摘要：
X-ray transients on sub-observation timescales represent a diverse and underexplored class of astrophysical phenomena, from stellar flares and magnetar bursts to extragalactic fast transients and supernova shock breakouts. We present a systematic search for such events across 20,212 Chandra ACIS observations using a new detection pipeline that combines source identification, light-curve analysis, catalogue cross-matching, and a novel statistical classifier, the cumulative distribution discriminator (CuDiDi). From 1420 initial candidates, we identified a high-confidence golden sample of 765 transients spanning a broad range of timescales, fluxes, and spectral shapes. The candidates are distributed across the whole sky and show a wide range of durations with a median of 10 ks. A subset of fast events lasting < 30 s displays very soft spectra and is likely due to flaring dwarf stars, although extragalactic phenomena cannot be ruled out for all of them. The comparison with previously published samples showed that CuDiDi identifies most known transients while imposing somewhat stricter variability criteria, and it also extends the total sample of Chandra transients to include shorter events. We deliver a comprehensive catalogue of sub-observation Chandra X-ray transients and establish a general method for exploiting archival datasets to uncover rare short-lived high-energy phenomena.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
