改进核心坍缩超新星的引力波搜索的背景：一种机器学习方法
Improving the background of gravitational-wave searches for core collapse supernovae: A machine learning approach
摘要：
基于之前的O 1-O2观测运行，预计Advanced LIGO和Virgo在下一次观测运行中收集的数据中约有30%是单干涉仪数据，即，当网络中只有一个探测器在观测模式下工作时，它们将被收集。超新星事件产生的引力波信号的处理不依赖于匹配滤波技术，因为信号具有随机性。如果一颗银河系超新星发生在单干涉仪时间，由于探测器之间缺乏相干性，将其未建模的引力波信号从噪声中分离出来将更加困难。我们提出了一种新的机器学习方法来执行基于标准LIGO-Virgo coherentWave-Burst管道的单干涉仪超新星搜索。结果表明，该方法可以有效地区分银河系引力波超新星信号和噪声瞬变，降低搜索的虚警率，提高探测器的超新星探测范围。
英文摘要：
Based on the prior O1-O2 observing runs, about 30% of the data collected by Advanced LIGO and Virgo in the next observing runs are expected to be single-interferometer data, i.e., they will be collected at times when only one detector in the network is operating in observing mode. Searches for gravitational wave signals from supernova events do not rely on matched filtering techniques because of the stochastic nature of the signals. If a Galactic supernova occurs during single-interferometer times, separation of its unmodelled gravitational-wave signal from noise will be even more difficult due to lack of coherence between detectors. We present a novel machine learning method to perform single-interferometer supernova searches based on the standard LIGO-Virgo coherentWave-Burst pipeline. We show that the method may be used to discriminate Galactic gravitational-wave supernova signals from noise transients, decrease the false alarm rate of the search, and improve the supernova detection reach of the detectors.
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