引力波信号去噪的新型深度学习架构综述
Survey of Novel Deep Learning Architectures for Denoising Gravitational-wave Signals
浏览论文内容
中文总结 AI 辅助
本文首次在完整自旋双黑洞参数空间上受控比较五种神经网络去噪架构，提出多尺度频率感知架构，通过匹配并合光谱结构实现最佳保真度，并泛化到真实LIGO-Virgo-KAGRA数据。
中文摘要 AI 辅助
引力波去噪必须处理自旋、进动双星系统的全部多样性，因为恢复的波形是参数估计、广义相对论检验和群体研究的基础。匹配滤波可以实现这一目标，但随着下一代探测器推动事件率上升，其成本变得难以承受；深度学习提供实时重建，但当前方法是在狭窄的参数空间上开发的，妨碍了原则性比较和可靠部署。我们首次对五种神经网络架构进行了受控比较，用于引力波去噪，这些架构在完整的天体物理动机自旋双黑洞参数空间上以相同方式训练。一个统一原则浮现出来：将网络结构与并合的光谱解剖——旋近、并合、铃宕——相匹配，优于暴力模型缩放。我们的多尺度频率感知架构通过每个频率区域的专用并行分支体现了这一点，在使用比更大模型更少的参数的同时实现了最佳保真度。它无需重新训练即可从模拟训练泛化到真实的LIGO-Virgo-KAGRA数据，尽管仅在单个探测器的模拟噪声上训练，却能在跨越三个观测运行期和两个探测器的已确认事件中恢复并合形态。我们从去噪残差构建群体级不确定性带，验证其校准，并在扩展质量比和类似层级并合残余的自旋群体上对框架进行压力测试。应用于无已知信号的真实噪声时，网络几乎在所有地方抑制其输出，罕见的例外可追溯到噪声瞬变而非普遍弱点——表明该统计量区分信号与噪声，并激励未来的探测研究。发布的权重为未来扩展提供了可部署、可复现的基准。
英文摘要
Gravitational-wave denoising must handle the full diversity of spinning, precessing binaries, since the recovered waveform underpins parameter estimation, tests of general relativity, and population studies. Matched filtering achieves this at a cost that becomes prohibitive as next-generation detectors push event rates higher; deep learning offers real-time reconstruction, but current methods are developed on narrow parameter spaces, precluding principled comparison and reliable deployment. We present the first controlled comparison of five neural-network architectures for gravitational-wave denoising, trained identically across the full astrophysically-motivated spinning binary-black-hole parameter space. A unifying principle emerges: matching network structure to the spectral anatomy of a coalescence -- inspiral, merger, ringdown -- outperforms brute-force model scaling. Our Multi-Scale Frequency-Aware architecture embodies this via dedicated parallel branches per frequency regime, achieving the best fidelity while using fewer parameters than larger models. It generalizes from simulated training to real LIGO-Virgo-KAGRA data without retraining, recovering merger morphology across confirmed events spanning three observing runs and both detectors, despite training on a single detector's simulated noise. We construct population-level uncertainty bands from denoising residuals, validate their calibration, and stress-test the framework on extended mass ratios and a spin population resembling hierarchical-merger remnants. Applied to real noise with no known signal, the network suppresses its output almost everywhere, with rare exceptions traced to noise transients rather than a general weakness -- indicating the statistic discriminates signal from noise and motivating a future detection study. Released weights give a deployable, reproducible benchmark for future extensions.
发表机构
- Indian Institute of Science（印度科学学院）
- Tata Institute of Fundamental Research（塔塔基础研究所）
机构由 AI 辅助整理，请以论文原文为准。
