论文标题
在第三次观察中，使用机器学习增强了高级Ligo-Virgo的第三次观察中搜索二进制黑洞合并
Search for binary black hole mergers in the third observing run of Advanced LIGO-Virgo using coherent WaveBurst enhanced with machine learning
论文作者
论文摘要
在这项工作中，我们使用机器学习（ML）增强的连贯的波爆（CWB）管道来搜索第三次观测运行（O3）的高级Ligo-Virgo菌株数据中的二进制黑洞（BBH）合并。我们检测到具有同等意义或更高意义的所有引力波（GW）事件先前由标准CWB搜索在第三GW瞬态目录中对BBH合并（GWTC-3）中的BBH合并。 ML增强的CWB搜索确定了来自标准CWB搜索以前错过的目录中的五个其他GW候选事件。此外，我们确定了GWTC-3中未列出的三个边缘候选事件。对于在基准量中均匀分布的模拟事件，我们将相对于标准CWB搜索的检测效率提高了大约20美元\％$ $ $ $ $ $ $均为恒星质量和中间的质量黑洞二进制合并，并以虚假警报率为$ 1 \，$ 1 \，\ Mathrm {yrm {yr {yr {yr}^^{-1} $。与标准的CWB搜索相比，我们通过报告对旋转不必要和偏心的BBH事件的敏感性提高了对通用BBH信号检测的ML增强搜索的鲁棒性。此外，我们比较了ML增强CWB搜索不同检测器网络的改进。
In this work, we use the coherent WaveBurst (cWB) pipeline enhanced with machine learning (ML) to search for binary black hole (BBH) mergers in the Advanced LIGO-Virgo strain data from the third observing run (O3). We detect, with equivalent or higher significance, all gravitational-wave (GW) events previously reported by the standard cWB search for BBH mergers in the third GW Transient Catalog (GWTC-3). The ML-enhanced cWB search identifies five additional GW candidate events from the catalog that were previously missed by the standard cWB search. Moreover, we identify three marginal candidate events not listed in GWTC-3. For simulated events distributed uniformly in a fiducial volume, we improve the detection efficiency with respect to the standard cWB search by approximately $20\%$ for both stellar-mass and intermediate mass black hole binary mergers, detected with a false-alarm rate less than $1\,\mathrm{yr}^{-1}$. We show the robustness of the ML-enhanced search for detection of generic BBH signals by reporting increased sensitivity to the spin-precessing and eccentric BBH events as compared to the standard cWB search. Furthermore, we compare the improvement of the ML-enhanced cWB search for different detector networks.
