深度学习探测引力波信号中的广义相对论外偏差：基于真实LIGO噪声的探测阈值研究
Deep Learning Detection of Beyond-General-Relativity Deviations in Gravitational-Wave Signals: A Detection-Threshold Study with Real LIGO Noise
- Concordia University（康考迪亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用混合CNN分类器在真实LIGO噪声中探测引力波信号的广义相对论外偏差，确定探测阈值约为β≈0.25，并指出小偏差时的负结果量化了纯机器学习搜索的局限。
AI中文摘要：
我们研究了利用机器学习探测引力波信号中受控的广义相对论外（beyond-GR）偏差，同时使用合成的aLIGO功率谱密度噪声和真实LIGO H1探测器应变数据。我们将三种偏差族应用于广义相对论的旋进-合并-铃宕波形：振幅调制、相位调制和频率调制，每种偏差由一个无量纲强度系数$\beta$参数化。我们训练了一个混合分类器，结合一维卷积神经网络与十个手工设计的波形统计量，在GR和修改后的波形上进行训练，并在训练中未包含的偏差类型上进行测试。核心结果是作为$\beta$函数的定量可探测性曲线。使用真实的GW150914应变作为模板和真实H1探测器噪声，我们发现探测阈值约为$\beta \approx 0.25$，准确率从$\beta \leq 0.2$时的随机水平平滑上升到$\beta \geq 0.5$时的完美分类。该阈值特定于此处采用的随时间二次方调制形式，不应被解释为对广义相对论外参数的通用约束。尽管如此，我们认为小$\beta$时的负结果具有信息量：它确立了在没有匹配滤波信号提取的情况下，仅基于机器学习的广义相对论外搜索在真实探测器噪声中的定量限制。
英文摘要：
We study machine-learning detection of controlled beyond-General-Relativity (beyond-GR) deviations in gravitational-wave signals, using both synthetic aLIGO-PSD noise and real LIGO H1 detector strain. Three deviation families are applied to General-Relativistic inspiral-merger-ringdown waveforms: amplitude modulation, phase modulation, and frequency modulation, each parameterized by a dimensionless strength coefficient $β$. A hybrid classifier combining a one-dimensional convolutional neural network with ten hand-crafted waveform statistics is trained on GR and modified waveforms and tested on a deviation type excluded from training. The central result is a quantitative detectability curve as a function of $β$. Using the real GW150914 strain as a template and real H1 detector noise, we find a detection threshold at $β\approx 0.25$, with accuracy rising smoothly from chance at $β\leq 0.2$ to perfect classification at $β\geq 0.5$. The threshold value is specific to the quadratic-in-time modulation form adopted here and should not be interpreted as a generic constraint on beyond-GR parameters. We nevertheless argue that the negative result at small $β$ is informative: it establishes a quantitative limit on machine-learning-only beyond-GR searches in real detector noise, in the absence of matched-filter signal extraction.
