用可训练滤波器检测射电星系异常形态，无需标注异常样本。
Anomaly detection in radio galaxy data with trainable COSFIRE filters
- 用可训练的COSFIRE滤波器提取射电星系形态特征
- 在基准数据集上达到79%的几何平均得分，优于深度自编码器
- 适合处理海量数据且需发现未知异常的射电天文任务
射电天文学中的异常检测因数据量庞大且异常样本极少而面临挑战。本文提出一种基于射电源形态特征的创新异常检测方法，采用可训练的COSFIRE（Shifted Filter Responses组合）滤波器作为复杂深度学习模型的高效替代方案。该框架将COSFIRE描述子与无监督局部离群因子（LOF）算法结合，识别异常的射电星系形态。在射电星系基准数据集上的评估表明，该方法取得79%的几何平均（G-Mean）得分，优于计算成本更高的深度自编码器（77%）。该半监督方法通过建模正常模式并检测偏离，无需训练集中包含异常样本，克服了传统监督方法的主要局限。该方法对下一代射电望远镜在快速处理和发现未知现象方面具有重要应用前景。
原文摘要 · Abstract (English)
Detecting anomalies in radio astronomy is challenging due to the vast amounts of data and the rarity of labeled anomalous examples. Addressing this challenge requires efficient methods capable of identifying unusual radio galaxy morphologies without relying on extensive supervision. This work introduces an innovative approach to anomaly detection based on morphological characteristics of the radio sources using trainable COSFIRE (Combination of Shifted Filter Responses) filters as an efficient alternative to complex deep learning methods. The framework integrates COSFIRE descriptors with an unsupervised Local Outlier Factor (LOF) algorithm to identify unusual radio galaxy morphologies. Evaluations on a radio galaxy benchmark data set demonstrate strong performance, with the COSFIRE-based approach achieving a geometric mean (G-Mean) score of 79%, surpassing the 77% achieved by a computationally intensive deep learning autoencoder. By characterizing normal patterns and detecting deviations, this semi-supervised methodology overcomes the need for anomalous examples in the training set, a major limitation of traditional supervised methods. This approach shows promise for next-generation radio telescopes, where fast processing and the ability to discover unknown phenomena are crucial.
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