Detecting localized density differences in multivariate data is a crucial task in computational science. Such anomalies can indicate a critical system failure, lead to a groundbreaking scientific discovery, or reveal unexpected changes in data distribution. We introduce EagleEye, an anomaly detection method to compare two multivariate datasets with the aim of identifying local density anomalies, namely over- or under-densities affecting only localised regions of the feature space. Anomalies are detected by modelling, for each point, the ordered sequence of its neighbours' membership label as a coin-flipping process and monitoring deviations from the expected behaviour of such process. A unique advantage of our method is its ability to provide an accurate, entirely unsupervised estimate of the local signal purity. We demonstrate its effectiveness through experiments on both synthetic and real-world datasets. In synthetic data, EagleEye accurately detects anomalies in multiple dimensions even when they affect a tiny fraction of the data. When applied to a challenging resonant anomaly detection benchmark task in simulated Large Hadron Collider data, EagleEye successfully identifies particle decay events present in just 0.3% of the dataset. In global temperature data, EagleEye uncovers previously unidentified, geographically localised changes in temperature fields that occurred in the most recent years. Thanks to its key advantages of conceptual simplicity, computational efficiency, trivial parallelisation, and scalability, EagleEye is widely applicable across many fields.

翻译：检测多变量数据中的局部密度差异是计算科学中的关键任务。此类异常可能指示关键系统故障、促成突破性科学发现，或揭示数据分布中的意外变化。本文提出EagleEye异常检测方法，通过比较两个多变量数据集来识别局部密度异常，即仅影响特征空间局部区域的过密或欠密现象。该方法通过将每个数据点的邻居成员标签有序序列建模为硬币翻转过程，并监测该过程与预期行为的偏差来实现异常检测。本方法的独特优势在于能够提供完全无监督的局部信号纯度精确估计。我们通过合成数据集和真实数据集的实验验证了其有效性：在合成数据中，EagleEye能准确检测多维空间中的异常，即使这些异常仅影响极小比例的数据；应用于模拟大型强子对撞机数据中具有挑战性的共振异常检测基准任务时，该方法成功识别出仅占数据集0.3%的粒子衰变事件；在全球温度数据中，EagleEye揭示了近年来出现的、先前未被识别的温度场地理局部变化。凭借其概念简洁性、计算高效性、易并行性和可扩展性等关键优势，EagleEye具有广泛的跨领域适用性。