改善核坍缩超新星引力波搜索的本底：一种机器学习方法
天体物理仪器与方法
2020-06-14 v1
摘要
基于先前的 O1-O2 观测运行，预计 Advanced LIGO 与 Virgo 在后续观测运行中收集的数据约有 30% 为单干涉仪数据，即这些数据是在网络中仅有一个探测器处于观测模式时收集的。由于信号的随机性质，针对超新星事件的引力波信号搜索并不依赖匹配滤波技术。若银河系超新星发生于单干涉仪时段，由于缺乏探测器间的相干性，将其未建模的引力波信号从噪声中分离将更为困难。我们提出了一种基于标准 LIGO-Virgo coherentWave-Burst 流水线的新型机器学习方法以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系引力波超新星信号与噪声瞬变，降低搜索的误报率，并提升探测器的超新星探测距离。
引用
@article{arxiv.2002.04591,
title = {Improving the background of gravitational-wave searches for core collapse supernovae: A machine learning approach},
author = {Marco Cavaglia and Sergio Gaudio and Travis Hansen and Kai Staats and Marek Szczepanczyk and Michele Zanolin},
journal= {arXiv preprint arXiv:2002.04591},
year = {2020}
}
备注
This is the version of the article before peer review or editing, as submitted by an author to Machine Learning: Science and Technology. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://iopscience.iop.org/article/10.1088/2632-2153/ab527d