Optimizing transient discovery with Swift-XRT
利用 Swift-XRT 优化暂现源发现
The Living Swift-XRT Point Source Catalogue (LSXPS) enables near real-time searches for X-ray transients. Many detected candidates are faint, often near the XRT detection limit, and are classed as 'low significance', as it is often unclear whether their apparent brightening reflects a genuine transient or a statistical fluctuation. Some of these sources are affected by Eddington bias, a statistical effect that inflates measured fluxes near the detection threshold. We present a simulation-based Bayesian framework that corrects for this bias and provides more accurate probabilities for each source being truly transient, i.e. that its true intensity exceeds the historical 3<inline-formula><tex-math>$\sigma$</tex-math></inline-formula> upper limit. Applied to LSXPS data, this method yields more reliable classifications, recovering over 500 transients above this threshold ─ more than an eight-fold increase over the original confirmed sample. Using extensive simulations based on real Swift-XRT images, we validate the robustness of this approach, showing that it remains stable across varying exposure times and background conditions. These results demonstrate that the LSXPS transient probabilities, corrected for Eddington bias, provide a reliable and internally consistent framework for real-time X-ray transient identification.
展开 ▾基于真实 Swift-XRT 图像的大规模模拟,系统校正了低计数源流量高估的埃丁顿偏差,结合贝叶斯推断和 log N–log S 先验,将 LSXPS 中“低显著性”暂现源候选体的确认数量从原 64 个提升至 500 个(保守 3σ),并验证了方法对不同曝光和背景条件的稳健性。
在 X 射线暂现源的实时搜寻中,LSXPS(Evans+ 2022)实现了自动候选标记,但低显著性样本常受埃丁顿偏差(Eddington 1940)困扰而难以去伪存真。本研究通过构建大规模仿真,结合 X 射线源计数分布(Mateos+ 2008)作为贝叶斯先验,有效修正了偏差,提供了更精确的暂现概率。未来该概率框架将被直接嵌入 LSXPS 的实时流水线,并有望应用于 Einstein Probe 等未来的时域巡天,提升高能瞬变的快速识别与跟进能力。
预印本 2026-06-29 · 刊出 2026-06-24 · 收录 2026-07-22