GW-FALCON：一种用于下一代引力波观测台早期预警警报的新型特征驱动深度学习方法
GW-FALCON: A Novel Feature-Driven Deep Learning Approach for Early Warning Alerts of BNS and NSBH Inspirals in Next-Generation GW Observatories
AI总结：
GW-FALCON通过特征驱动的深度学习方法，实现对下一代引力波观测台中BNS和NSBH inspiral的早期预警警报，利用特征提取和ANNs提升检测准确性和效率。
摘要：
下一代引力波观测台，如ET和CE，将能够以高信噪比和长带内持续时间检测双中子星（BNS）和中子星-黑洞（NSBH）的 inspiral，这使得系统性的早期预警警报既可行又具有科学价值。此类触发器对于协调快速电磁后续观测至关重要。在本工作中，我们介绍了一种名为GW-FALCON的新特征驱动深度学习框架，用于在下一代探测器中在引力波信号+噪声与纯噪声数据之间进行早期时间检测。与将原始时间序列输入CNN或更复杂的神经网络架构不同，我们首先使用TSFEL库从短观测时间窗口中提取大量统计、时间序列和频谱量。所得固定长度的特征向量随后被用作输入，馈送到适合低延迟操作的前馈ANNs中。我们通过将模拟的BNS和NSBH inspiral波形注入到由ET和CE设计的色噪声中，展示了该方法。我们分别在特征集上训练ANNs，这些特征集来自具有不同最大瞬时频率的partial-inspiral窗口，使预警触发可在合并前的几十到几百秒内发生。在所有探测器配置和数据集中，所得到的分类器实现了高准确性和检测效率，ET类网络通常在测试准确率达到约90%，而CE类网络在低误报概率下超过97%。据我们所知，这项工作首次提出了一个全面的基于特征的深度学习检测框架，用于下一代引力波观测台，将从应变时间序列数据中提取的特征连接到在可扩展到真实数据和更高级神经网络架构的设置中进行稳健的信号-噪声分类。
英文摘要：
Next-generation GW observatories such as the ET and CE will detect BNS and NSBH inspirals with high SNRs and long in-band durations, making systematic early-warning alerts both feasible and scientifically valuable. Such triggers are essential for coordinating rapid electromagnetic follow-up. In this work, we introduce GW-FALCON, a novel feature-driven DL framework for early-time detection between GW signal+noise and noise-only data in next-generation detectors. Instead of feeding raw time series to CNN or more complex neural network architectures, we first extract a large set of statistical, temporal, and spectral quantities from short observational time windows using the TSFEL library. The resulting fixed-length feature vectors are then used as input to feed-forward ANNs suitable for low-latency operation. We demonstrate the method using simulated BNS and NSBH inspiral waveforms injected into colored Gaussian noise generated from the ET and CE design PSDs. We train separate ANNs on feature sets extracted from partial-inspiral windows characterized by different maximum instantaneous frequencies, enabling early-warning triggers from tens to hundreds of seconds before merger. Across all detector configurations and datasets, the resulting classifiers achieve high accuracy and detection efficiency, with ET-like networks typically reaching test accuracies of order 90% and CE-like ones exceeding 97% at low false-alarm probability. To the best of our knowledge, this work presents the first comprehensive feature-based DL detection framework for Next-generation GW observatories, connecting feature extraction from strain time series data to robust signal-noise classification within a setup that can be extended to real data and to more advanced neural network architectures.
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