发表机构
Royal Holloway, University of London; University of Southampton（伦敦大学皇家霍洛威学院; 南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对引力波探测中多流水线输出结合及分布偏移问题，提出加权保形预测框架，通过似然比重新加权恢复校准覆盖并提高阈值附近事件置信度，恢复被遗漏的真实信号。
AI 中文摘要
在过去十年中，公里级干涉引力波探测器已观测到数百次紧凑双星并合，其中大多数是双黑洞。然而，数据以噪声为主，因此使用多种独立的搜索算法（流水线）来提高灵敏度和增强稳健性。我们没有采用选择最显著流水线输出的标准方法，而是使用基于保形预测的框架结合所有流水线的输出，为候选事件提供统计上严格的置信度估计。虽然结合流水线提高了灵敏度和排序稳健性，但它需要一个原则性的统计框架，该框架在观测运行期间数据特性演变时仍然有效。一个关键挑战是用于训练和校准的模拟数据集与用于测试的真实无标注观测数据之间的分布偏移，这可能使覆盖保证失效并使置信度估计产生偏差。在这项工作中，我们通过将似然比重新加权纳入我们的保形预测框架来解决这一挑战，以考虑协变量偏移。使用包含模拟信号的模拟数据集，我们证明加权保形预测在协变量偏移下恢复了良好校准的覆盖，并提高了接近探测阈值事件的置信度，从而恢复了原本会被遗漏的真实信号。
英文摘要
In the last decade, kilometre-scale interferometric gravitational-wave detectors have observed hundreds of compact binary mergers, the majority of which are binary black holes. However, the data are noise-dominated, and multiple independent search algorithms (pipelines) are used to enhance sensitivity and improve robustness. Rather than the standard approach of selecting the most significant pipeline output, we combine the outputs from all pipelines using a conformal prediction-based framework to provide statistically rigorous confidence estimates for candidate events. While combining pipelines improves sensitivity and ranking robustness, it requires a principled statistical framework that remains valid as data properties evolve across observing runs. A key challenge is distribution shifts between simulated datasets used for training and calibration and the real, unlabelled, observations used for testing, which can invalidate coverage guarantees and bias confidence estimates. In this work, we address this challenge by incorporating likelihood-ratio reweighting into our conformal prediction framework to account for covariate shift. Using mock datasets containing simulated signals, we demonstrate that weighted conformal prediction restores well-calibrated coverage under covariate shift and increases the confidence of events near the detection threshold, recovering true signals that would otherwise be missed.
Journal refProceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:937-957, 2026
