发表机构
Vanderbilt University（范德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究推出基于Transformer的引力波搜寻流水线\ucdcastor\uc02c，其可低成本估算虚警率，在灵敏度上显著优于GW-Whisper，且背景估算成本降低20倍，为引力波搜寻提供了实用可扩展方案。
AI 中文摘要
未来十年致密双星并合的探测率预计将提升，因此亟需开发匹配滤波的快速、鲁棒且可扩展的替代方案用于引力波搜寻。Transformer模型已彻底改变自然语言与音频处理领域，但其在引力波天文学中的应用仍未得到充分探索。本研究推出了\ucdcastor\uc02c，一种基于Transformer的符合搜索流水线，用于从先进LIGO探测器中探测双黑洞引力波信号。该模型的主要特性之一是能够通过时间滑动（time slides）低成本估算虚警率，无需重复评估神经网络。我们在机器学习引力波搜寻挑战赛（MLGWSC-1）的数据集以及约5个月的真实O3b观测应变数据上对\ucdcastor\uc02c进行评估。在基准数据集测试中，\ucdcastor\uc02c跻身最灵敏的机器学习流水线之列，且成功恢复了GWTC-3目录中位于其训练范围内的大部分可信事件。我们还将\ucdcastor\uc02c与另一种Transformer架构GW-Whisper（OpenAI音频基础模型的领域适配版本）进行基准测试，发现\ucdcastor\uc02c在灵敏度上显著优于经改造的音频模型，同时将背景估算的计算成本降低了20倍。我们的结果证明了一种高度实用、可扩展的深度学习引力波搜寻方法，以及用于未来观测运行的经验背景估算方案。
英文摘要
With the projected increase in the detection rate of compact-binary coalescences in the coming decade, there is critical need to develop fast, robust, and scalable alternatives to matched filtering for gravitational-wave searches. Transformer models have revolutionized natural language and audio processing but their application to gravitational-wave astronomy is still largely unexplored. In this work, we introduce \castor, a transformer-based coincident search pipeline for detecting binary black hole gravitational-wave signals from Advanced LIGO detectors. One of the major features of our model is that it allows the false-alarm rate to be estimated via time slides cheaply without requiring repeated evaluations of the neural network. We evaluate \castor\ on datasets from the Machine-Learning Gravitational-Wave Search Challenge (MLGWSC-1) and on approximately five months of real O3b observing strain. When tested on benchmark datasets, \castor\ ranks among the most sensitive machine-learning pipelines and successfully recovers the majority of confident events from the GWTC-3 catalog that lie within its training range. We also benchmark \castor\ against another transformer architecture, GW-Whisper, a domain-adaptation of OpenAI's audio foundation model. We find that \castor\ substantially outperforms the repurposed audio model in sensitivity and also reduces the computational cost of background estimation by a factor of 20. Our results demonstrate a highly practical, scalable approach for deep-learning gravitational wave searches and empirical background estimation for future observing runs.
