Dynamics signature based anomaly detection

Document Type

Conference Proceeding

Publication Date

1-1-2023

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.

Identifier

85119341890 (Scopus)

Publication Title

Software Practice and Experience

External Full Text Location

https://doi.org/10.1002/spe.3052

e-ISSN

1097024X

ISSN

00380644

First Page

160

Last Page

175

Issue

1

Volume

53

Grant

2109988

Fund Ref

National Science Foundation

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