基于特征签名的射电天文学数据新奇性检测
Novelty Detection on Radio Astronomy Data using Signatures
AI总结：
本研究提出半监督框架SigNova，结合签名变换、马氏距离新奇性评分与Pysegments分割算法检测流式数据中的射频干扰，在MWA、HERA等数据上验证了其优于经典加窗技术的检测效果。
摘要：
我们提出了SigNova，一种用于检测流式数据中异常的新型半监督框架。尽管我们的初始示例聚焦于在射电天文学领域的数字化信号中检测射频干扰（RFI），但需要注意的是，SigNova的适用范围可扩展至任何类型的流式数据。该框架包含三个主要组件。首先，我们使用签名变换从观测序列中提取一组规范的汇总统计量，这使我们能够将可变长度的可见度样本表示为有限维特征向量。其次，每个特征向量都会被赋予一个新奇性分数，该分数通过计算其与无RFI训练集中最近邻的马氏距离得到。通过对这些分数设置阈值，我们无需依赖严格的分布假设，就能识别出偏离无RFI可见度样本预期行为的观测区间。第三，我们将该异常检测器与分割算法Pysegments集成，以定位受RFI污染的连续观测（若存在的话）。这种方法为RFI检测中常用的经典加窗技术提供了一种极具竞争力的替代方案。重要的是，我们算法的复杂度取决于RFI的模式，而非观测窗口的大小。我们展示了SigNova如何提升在时频可见度数据中对各类RFI（例如宽带和窄带RFI）的检测效果。我们在默奇森宽场阵列（MWA）望远镜数据、模拟数据以及再电离时期氢阵列（HERA）上验证了该框架的有效性。
英文摘要：
We introduce SigNova, a new semi-supervised framework for detecting anomalies in streamed data. While our initial examples focus on detecting radio-frequency interference (RFI) in digitized signals within the field of radio astronomy, it is important to note that SigNova's applicability extends to any type of streamed data. The framework comprises three primary components. Firstly, we use the signature transform to extract a canonical collection of summary statistics from observational sequences. This allows us to represent variable-length visibility samples as finite-dimensional feature vectors. Secondly, each feature vector is assigned a novelty score, calculated as the Mahalanobis distance to its nearest neighbor in an RFI-free training set. By thresholding these scores we identify observation ranges that deviate from the expected behavior of RFI-free visibility samples without relying on stringent distributional assumptions. Thirdly, we integrate this anomaly detector with Pysegments, a segmentation algorithm, to localize consecutive observations contaminated with RFI, if any. This approach provides a compelling alternative to classical windowing techniques commonly used for RFI detection. Importantly, the complexity of our algorithm depends on the RFI pattern rather than on the size of the observation window. We demonstrate how SigNova improves the detection of various types of RFI (e.g., broadband and narrowband) in time-frequency visibility data. We validate our framework on the Murchison Widefield Array (MWA) telescope and simulated data and the Hydrogen Epoch of Reionization Array (HERA).
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