基于集成方法的GW231123在不同波形模型下的残差测试
Ensemble-Based Residual Tests of GW231123 across Waveform Models
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中文总结 AI 辅助
针对GW231123引力波事件，本研究提出基于集成方法的残差测试，将100个高似然波形纳入分析，结合三种拟合优度检验开展注入测试，实现跨模型波形差异的稳健检测。
中文摘要 AI 辅助
GW231123是一个特殊的引力波事件，不同波形模型对其源参数的推断结果存在显著差异。残差测试是评估每个波形模型是否充分描述观测信号的直接方法。本研究扩展了传统残差测试方法，针对每个模型减去100个似然最高的波形，而非仅减去最大似然波形，从而将波形重建不确定性引入残差分析。这种基于集成的方法将残差测试从单一波形诊断转变为对局部高似然波形流形的稳健性测试。我们还开展了注入测试，以量化真实探测器噪声中跨模型波形差异的可探测性。基于Kolmogorov-Smirnov检验、Anderson-Darling检验和Pearson卡方检验这三种拟合优度检验的残差测试框架，具有高速、低计算成本的特点，使得这些分析的大规模实施成为可能。
英文摘要
GW231123 is an exceptional gravitational wave event for which different waveform models yield significantly different inferred source parameters. Residual tests provide a direct way to assess whether each waveform model gives an adequate description of the observed signal. In this work, we extend the conventional residual-test methods by subtracting the 100 highest likelihood waveforms, rather than only the maximum likelihood waveform for each model, thereby propagating waveform reconstruction uncertainty into the residual analysis. This ensemble-based approach turns the residual test from a single waveform diagnostic into a robustness test over the local high likelihood waveform manifold. We further perform injection tests to quantify the detectability of cross-model waveform discrepancies in realistic detector noise. The large-scale implementation of these analyses is made possible by the high speed and low computational cost of our residual testing framework, which is based on three goodness-of-fit tests: the Kolmogorov-Smirnov test, the Anderson-Darling test, and Pearson's chi-squared test.
