Improving the background of gravitational-wave searches for core collapse supernovae: a machine learning approach
Improving the background of gravitational-wave searches for core collapse supernovae: a machine learning approach
复制标题
DOI：
10.1088/2632-2153/ab527d
复制
发表时间：
2020-03-01
影响因子：
6.8
通讯作者：
Zanolin, M.
中科院分区：
文献类型：
--
作者：
Cavaglia, M.;Gaudio, S.;Zanolin, M.
根据之前的O1-O2观测运行，Advanced LIGO和Virgo在接下来的观测运行中收集的数据中约有30%预计是单干涉仪数据，即当网络中只有一个探测器在观测模式下运行时收集这些数据。由于信号的随机性，对超新星事件引力波信号的搜索并不依赖于匹配滤波技术。如果银河超新星发生在单干涉仪时间内，由于探测器之间缺乏相干性，将其未建模的引力波信号与噪声分离将更加困难。我们提出了一种新颖的机器学习方法，用于基于标准 LIGO-Virgo 相干 WaveBurst 管道执行单干涉仪超新星搜索。我们表明，该方法可用于区分银河引力波超新星信号和噪声瞬变，降低搜索误报率，提高探测器的超新星探测范围。
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 coherent WaveBurst 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.