This content will become publicly available on November 15, 2022

Dynamics signature based anomaly detection
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.
Authors:
; ; ; ; ;
Award ID(s):
Publication Date:
NSF-PAR ID:
10311659
Journal Name:
Software: Practice and Experience
ISSN:
0038-0644
4. ABSTRACT We explore the synergy between photometric and spectroscopic surveys by searching for periodic variable stars among the targets observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) using photometry from the All-Sky Automated Survey for Supernovae (ASAS-SN). We identified 1924 periodic variables among more than $258\, 000$ APOGEE targets; 465 are new discoveries. We homogeneously classified 430 eclipsing and ellipsoidal binaries, 139 classical pulsators (Cepheids, RR Lyrae, and δ Scuti), 719 long-period variables (pulsating red giants), and 636 rotational variables. The search was performed using both visual inspection and machine learning techniques. The light curves were also modelled with the damped random walk stochastic process. We find that the median [Fe/H] of variable objects is lower by 0.3 dex than that of the overall APOGEE sample. Eclipsing binaries and ellipsoidal variables are shifted to a lower median [Fe/H] by 0.2 dex. Eclipsing binaries and rotational variables exhibit significantly broader spectral lines than the rest of the sample. We make ASAS-SN light curves for all the APOGEE stars publicly available and provide parameters for the variable objects.