PyCBC Live 的天体物理源分类与距离估计
Astrophysical Source Classification and Distance Estimation for PyCBC Live
AI总结：
本研究在 PyCBC Live 中开发了一种基于啁啾质量和距离估计的 CBC 源快速分类方法，以支持引力波事件的即时多信使后续观测。
摘要：
在先进 LIGO 和先进 Virgo 探测器的第三次观测运行（O3）期间，已编录了数十个候选引力波（GW）事件。本次观测运行的一个挑战是快速识别并公开发布致密双星并合（CBC）信号，这一任务由诸如 PyCBC Live 之类的低延迟搜索执行。在 O3 后期，我们开发了一种在 PyCBC Live 内对 CBC 源进行分类的方法，通过其包含中子星或黑洞成分的概率进行分类，以便于电磁和中微子天文台进行即时后续观测。这种快速分类使用搜索恢复的啁啾质量作为输入，因为对于较低质量的双星系统，难以高精度测量质量比。我们还利用从搜索输出中导出的距离估计来校正因宇宙学红移导致的啁啾质量偏差。我们展示了模拟信号的结果，以及在 O3 期间低延迟确认的候选事件的结果。
英文摘要：
During the third observing run (O3) of the Advanced LIGO and Advanced Virgo detectors, dozens of candidate gravitational-wave (GW) events have been catalogued. A challenge of this observing run has been the rapid identification and public dissemination of compact binary coalescence (CBC) signals, a task carried out by low-latency searches such as PyCBC Live. During the later part of O3, we developed a method of classifying CBC sources, via their probabilities of containing neutron star or black hole components, within PyCBC Live, in order to facilitate immediate follow-up observations by electromagnetic and neutrino observatories. This fast classification uses the chirp mass recovered by the search as input, given the difficulty of measuring the mass ratio with high accuracy for lower-mass binaries. We also use a distance estimate derived from the search output to correct for the bias in chirp mass due to the cosmological redshift. We present results for simulated signals, and for confirmed candidate events identified in low latency over O3.
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