Real-Time Multi-Mode Post-Merger Gravitational Wave Detection using Convolutional Neural Networks: Methodology Development for Third-Generation Detectors
Department of Astronomy & Astrophysics, The Pennsylvania State University（天文学与天体物理系，宾夕法尼亚州立大学）
The detection and characterization of post-merger gravitational wave signals from binary neutron star mergers remains challenging with current ground-based detectors. We present a convolutional neural network framework designed for real-time detection and multi-mode frequency extraction of post-merger signals, achieving an inference latency of 3.0 ms and a frequency accuracy of 48.6 Hz on the direct-comparison subsets (53 Hz on the comprehensive test set). The framework is validated on realistic LIGO O4 detector noise including authentic GravitySpy glitch morphologies, demonstrating ROC AUC of 0.999999 and 99.998% detection efficiency at 1% false alarm rate. These exceptional performance metrics arise from an aggressive training augmentation strategy that exposes the network to artificially challenging conditions, enabling robust generalization to our synthetic O4 detector noise model. We compare performance against a simplified matched filtering baseline using Lorentzian templates (23x more accurate despite a 4.7x computational overhead) and Bayesian parameter estimation (1.2 million times faster), establishing complementary trade-offs in the analysis landscape. While current O4 sensitivity limits post-merger detections to ~20 Mpc (~1 detection per century), this methodology provides essential infrastructure for third-generation detectors (Einstein Telescope, Cosmic Explorer) where post-merger detection will become routine with annual detection rates exceeding 100 events. Our validation framework identifies expected behavior in uncertainty scaling that reflects realistic training constraints rather than idealized Fisher information limits, demonstrating honest assessment practices for machine learning applications in gravitational wave astronomy.
A Search for Binary Black Hole Mergers in LIGO O1-O3 Data with Convolutional Neural Networks
Harvard University（哈佛大学）；The NSF AI Institute for Artificial Intelligence and Fundamental Interactions（美国国家科学基金会人工智能基础相互作用研究所）；Faculty of Arts and Sciences Research Computing, Harvard University（哈佛大学本科研究院计算研究部）；Center for Astrophysics — Harvard & Smithsonian（哈佛-史密森天体物理中心）
Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
Application of hidden Markov model tracking to the search for long-duration transient gravitational waves from the remnant of the binary neutron star merger GW170817
A Simulation Driven Deep Learning Approach for Separating Mergers and Star Forming Galaxies: The Formation Histories of Clumpy Galaxies in all the CANDELS Fields
Convolutional Neural Networks for signal detection in real LIGO data
Friedrich-Schiller-Universität Jena（弗里德里希·席勒大学耶拿分校）；Michael Stifel Center Jena（耶拿迈克尔·斯蒂费尔中心）；Astronomical Institute of the Czech Academy of Sciences（捷克科学院天文研究所）；Max-Planck-Institut für Gravitationsphysik, Albert-Einstein-Institut（马克斯·普朗克引力物理研究所（阿尔伯特·爱因斯坦研究所））；Leibniz Universität Hannover（莱布尼茨汉诺威大学）
