AI 中文总结
研究利用监督机器学习与共形预测结合，将多管道信息合并为置信度分数，应用于GWTC目录事件，识别出亚阈值候选者，为引力波目录和实时警报提供简化的候选评估方法。
AI 中文摘要
瞬态引力波信号的检测依赖于独立搜索算法分析探测器数据并为候选事件赋予显著性度量。然而，性能差异使其解读复杂化。我们使用监督机器学习结合共形预测（一种量化不确定性的框架）将多管道信息合并为校准良好的置信度分数。我们证明该方法在不同分类器架构上都很稳健，在不同模拟数据集上训练时保持稳定。应用于GWTC目录中的事件时，该框架识别出几个具有更高置信度的亚阈值候选者，包括双中子星候选者GW200311_103121。我们检查这些上调的可靠性，发现高置信度预测对应信号类事件的证据。该框架通过为每个候选者提供单一校准良好的置信度度量，实现对引力波目录和实时警报的简化系统候选评估。
英文摘要
The detection of transient gravitational wave signals relies on independent search algorithms that analyse detector data and assign significance measures to candidate events. However, varying performance complicates their interpretation. We use supervised machine learning combined with conformal prediction, a framework to quantify uncertainties, to merge multi-pipeline information into well-calibrated confidence scores. We demonstrate that this approach is robust across different classifier architectures and remains stable when trained on different simulated datasets. When applied to events across the GWTC catalogue up to and including the second part of the fourth observing run, the framework identifies several subthreshold candidates with elevated confidence, including the binary neutron star candidate GW200311_103121. We examine the reliability of these up-rankings, finding evidence that high-confidence predictions correspond to signal-like events. This framework enables simplified systematic candidate assessment for gravitational wave catalogues and real-time alerts by providing a single, well-calibrated confidence measure per candidate.
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