Dynamics signature based anomaly detection

Abstract

Identifying anomalies, especially weak anomalies in constantly changing targets, is more difficult than in stable targets. In this article, we borrow the dynamics metrics and propose the concept of dynamics signature (DS) in multi‐dimensional feature space to efficiently distinguish the abnormal event from the normal behaviors of a variable star. The corresponding dynamics criterion is proposed to check whether a star's current state is an anomaly. Based on the proposed concept of DS, we develop a highly optimized DS algorithm that can automatically detect anomalies from millions of stars' high cadence sky survey data in real‐time. Microlensing, which is a typical anomaly in astronomical observation, is used to evaluate the proposed DS algorithm. Two datasets, parameterized sinusoidal dataset containing 262,440 light curves and real variable stars based dataset containing 462,996 light curves are used to evaluate the practical performance of the proposed DS algorithm. Experimental results show that our DS algorithm is highly accurate, sensitive to detecting weak microlensing events at very early stages, and fast enough to process 176,000 stars in less than 1 s on a commodity computer.

Special issue on new trends in high‐performance computing: Software systems and applications

“…The eighth paper titled “Dynamics Signature based Anomaly Detection” by Bader et al 8 borrows the dynamics metrics and proposes the concept of Dynamics Signature (DS) in multi‐dimensional feature space to efficiently distinguish the abnormal event from the normal behaviors of a variable star. Two datasets, parameterized sinusoidal dataset containing 262,440 light curves, and a real variable star‐based dataset containing 462,996 light curves are used to evaluate the practical performance of the proposed DS algorithm.…”