Improving the Sensitivity of Gravitational Wave Detection with Weighted Conformal Prediction

2026-09-10

AI Summary

为解决引力波检测中的噪声问题和数据分布偏移挑战，本文采用基于加权共形预测的方法结合多种搜索算法，提高了检测灵敏度和结果的可靠性。

Abstract

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.

Problem

• gravitational wave detection
• pipeline
• conformal prediction
• covariate shift
• confidence estimates

Innovation

• weighted conformal prediction
• covariate shift
• likelihood-ratio reweighting