Search for binary black hole mergers in the third observing run of Advanced LIGO-Virgo using coherent WaveBurst enhanced with machine learning
Search for binary black hole mergers in the third observing run of Advanced LIGO-Virgo using coherent WaveBurst enhanced with machine learning
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DOI：
10.1103/physrevd.105.083018
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发表时间：
2022-01
影响因子：
5
通讯作者：
T. Mishra;B. O'Brien;M. Szczepańczyk;G. Vedovato;S. Bhaumik;V. Gayathri;G. Prodi;F. Salemi
中科院分区：
文献类型：
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作者：
T. Mishra;B. O'Brien;M. Szczepańczyk;G. Vedovato;S. Bhaumik;V. Gayathri;G. Prodi;F. Salemi
在这项工作中，我们使用通过机器学习 (ML) 增强的相干 WaveBurst (cWB) 管道来搜索来自第三次观测运行 (O3) 的高级 LIGO-Virgo 应变数据中的二元黑洞 (BBH) 合并。我们以同等或更高的意义检测到先前由标准 cWB 搜索第三个 GW 瞬态目录 (GWTC-3) 中的 BBH 合并所报告的所有引力波 (GW) 事件。 ML 增强型 cWB 搜索从目录中识别出先前被标准 cWB 搜索遗漏的五个额外 GW 候选事件。此外，我们还确定了 GWTC-3 中未列出的三个边缘候选事件。对于均匀分布在基准体积中的模拟事件，我们将恒星质量和中等质量黑洞双星合并的检测效率相对于标准 cWB 搜索提高了大约 $20\%$，检测到的误报率小于 $1\,\mathrm{yr}^{-1}$。与标准 cWB 搜索相比，我们通过报告对自旋进动和偏心 BBH 事件的敏感性增加，展示了用于检测通用 BBH 信号的 ML 增强搜索的鲁棒性。此外，我们还比较了不同检测器网络的 ML 增强 cWB 搜索的改进。
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.