With the increasing volume of astronomical data generated by modern survey
telescopes, automated pipelines and machine learning techniques have become
crucial for analyzing and extracting knowledge from these datasets. Anomaly
detection, i.e. the task of identifying irregular or unexpected patterns in the
data, is a complex challenge in astronomy. In this paper, we propose
Multi-Class Deep Support Vector Data Description (MCDSVDD), an extension of the
state-of-the-art anomaly detection algorithm One-Class Deep SVDD, specifically
designed to handle different inlier categories with distinct data
distributions. MCDSVDD uses a neural network to map the data into hyperspheres,
where each hypersphere represents a specific inlier category. The distance of
each sample from the centers of these hyperspheres determines the anomaly
score. We evaluate the effectiveness of MCDSVDD by comparing its performance
with several anomaly detection algorithms on a large dataset of astronomical
light-curves obtained from the Zwicky Transient Facility. Our results
demonstrate the efficacy of MCDSVDD in detecting anomalous sources while
leveraging the presence of different inlier categories. The code and the data
needed to reproduce our results are publicly available at
this https URL

使用多类深度支持向量数据描述算法 (MCDSVDD) 在天文学中进行异常检测，并通过与其他异常检测算法在大型天文曲线数据集上的性能比较验证了其有效性。