Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid release of sky localization information are crucial for the success of these electromagnetic follow-up observations. However, searches for gravitational wave candidates in real time suffer from a nonnegligible false alarm rate. By leveraging the sky localization information and other metadata associated with gravitational wave candidates, GWSkyNet, a machine-learning classifier developed by Cabero et al., demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO–Virgo–KAGRA's (LVK) fourth observing run by reviewing and updating the architecture and features used as inputs by the algorithm. We also retrain and fine-tune the classifier with data from the third observing run. To improve the prospect of electromagnetic follow-up observations, we incorporate GWSkyNet into LVK's low-latency infrastructure as an automatic pipeline for the evaluation of gravitational wave alerts in real time. We test the readiness of the algorithm on an LVK mock data challenge campaign. The results show that by thresholding on the GWSkyNet score, noise masquerading as astrophysical sources can be rejected efficiently and the majority of true astrophysical signals can be correctly identified.

中文翻译：

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GWSkyNet。二.用于公共引力波警报实时分类的精细机器学习管道


引力波事件的电磁跟踪观测提供了重要的见解，并从这种新型天体物理瞬变中获得了重大的科学成果。准确识别引力波候选者和快速发布天空定位信息对于这些电磁跟踪观测的成功至关重要。然而，实时搜索候选引力波会遇到不可忽视的误报率。通过利用天空定位信息和与候选引力波相关的其他元数据，GWSkyNet（Cabero 等人开发的机器学习分类器）在识别候选事件起源方面表现出了良好的准确性。我们通过审查和更新用作算法输入的架构和特征，提高了 LIGO–Virgo–KAGRA (LVK) 第四次观测运行的分类器的性能。我们还使用第三次观察运行的数据重新训练和微调分类器。为了改善电磁跟踪观测的前景，我们将 GWSkyNet 纳入 LVK 的低延迟基础设施，作为实时评估引力波警报的自动管道。我们在 LVK 模拟数据挑战活动中测试了算法的准备情况。结果表明，通过对 GWSkyNet 分数进行阈值处理，可以有效地拒绝伪装成天体物理源的噪声，并且可以正确识别大多数真实的天体物理信号。