干扰遇上推断：射电干扰的贝叶斯时间序列建模
Interference Meets Inference: Bayesian Time-Series Modeling of Radio-Frequency Interference
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中文总结 AI 辅助
本研究将射电频率干扰检测视为一维时间序列异常检测问题，提出概率混合模型框架，联合描述干净与污染成分，为每个时间样本赋予RFI状态的后验概率，生成软分类标签和季节性趋势，并在默奇森宽场阵列数据上验证，与SSINS硬标签相比提供相似或更优结果及不确定性度量。
中文摘要 AI 辅助
射电频率干扰（RFI）仍然是现代射电天文实验面临的主要挑战。在本工作中，我们将RFI检测视为一维时间序列异常检测问题，并开发了一个概率混合模型框架，用于将平滑变化的天文背景与异常污染分离开来。该模型联合描述了干净成分和污染成分，并为每个时间样本赋予其属于RFI状态的后验概率，而非仅依赖二元标记。这种概率化表述提供了分类置信度的度量，能够将不确定性传播到下游导出的统计量中，并为调查模糊事件提供额外信息。我们将该框架应用于2014年收集的默奇森宽场阵列观测数据，生成了“软”分类标签和季节性RFI趋势。我们使用天空扣除非相干噪声谱软件流水线（SSINS）进行了并行分析，该流水线产生“硬”分类标签。总体而言，混合模型提供了相似且在某些情况下更优的分类结果，同时提供了对RFI污染及其不确定性的互补概率描述。
英文摘要
Radio-frequency interference (RFI) remains a major challenge for modern radio astronomy experiments. In this work, we cast RFI detection as a one-dimensional time-series anomaly-detection problem and develop a probabilistic mixture-model framework for separating a smoothly varying astronomical background from anomalous contamination. The model jointly describes the clean and contaminated components and assigns each time sample a posterior probability of belonging to the RFI state, rather than relying solely on binary flags. This probabilistic formulation provides a measure of classification confidence, enables uncertainty propagation into derived downstream statistics, and offers additional information for investigating ambiguous events. We apply the framework to observations from the Murchison Widefield Array collected in 2014, producing "soft" classification labels and seasonal RFI trends. We perform a parallel analysis using the Sky-Subtracted Incoherent Noise Spectrum software pipeline (SSINS), which produces "hard" classification labels. Overall, the mixture model provides similar and in some cases superior classification results while providing a complementary probabilistic description of RFI contamination and its uncertainty.
发表机构
- Brown University（布朗大学）
- McGill University（麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。
