A classification algorithm for time-domain novelties in preparation for LSST alerts: Application to variable stars and transients detected with DECam in the Galactic Bulge

Abstract

With the advent of the Large Synoptic Survey Telescope (LSST), time-domain astronomy will be faced with an unprecedented volume and rate of data. Real-time processing of variables and transients detected by such large-scale surveys is critical to identifying the more unusual events and allocating scarce follow-up resources efficiently. We develop an algorithm to identify these novel events within a given population of variable sources. We determine the distributions of magnitude changes (dm) over time intervals (dt) for a given passband f, pf(dm|dt), and use these distributions to compute the likelihood of a test source being consistent with the population, or an outlier. We demonstrate our algorithm by applying it to the DECam multi-band time-series data of more than 2000 variable stars identified by Saha et al. (2019) in the Galactic Bulge that are largely dominated by long-period variables and pulsating stars. Our algorithm discovers 18 outlier sources in the sample, including a microlensing event, a dwarf nova, and two chromospherically active RS CVn stars, as well as sources in the Blue Horizontal Branch region of the color-magnitude diagram without any known counterparts. We compare the performance of our algorithm for novelty detection with multivariate KDE and Isolation Forest on the simulated PLAsTiCC dataset. We find that our algorithm yields comparable results despite its simplicity. Our method provides an efficient way for flagging the most unusual events in a real-time alert-broker system.

arXiv comment20 pages + Appendix, accepted to ApJ

References in corpus (30)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Astropy: A Community Python Package for Astronomy
• The NumPy array: a structure for efficient numerical computation
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• The SIMBAD astronomical database
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• A method for optimal image subtraction
• The Zwicky Transient Facility: Data Processing, Products, and Archive
• The Dark Energy Camera
• Real-Time Difference Imaging Analysis of MOA Galactic Bulge Observations During 2000
• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• Proper image subtraction - optimal transient detection, photometry and hypothesis testing
• A New Algorithm For Difference Image Analysis
• Chemical evolution of the Galactic bulge as traced by microlensed dwarf and subgiant stars. VI. Age and abundance structure of the stellar populations in the central sub-kpc of the Milky Way
• Photometric Supernova Classification With Machine Learning
• Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
• Automated Transient Identification in the Dark Energy Survey
• RAPID: Early Classification of Explosive Transients using Deep Learning
• Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
• Using Machine Learning for Discovery in Synoptic Survey Imaging
• Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
• Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream
• The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory
• The Microlensing Event Rate and Optical Depth Toward the Galactic Bulge from MOA-II
• A Naive Bayes Source Classifier for X-ray Sources
• Mira variables in the Galactic bulge with OGLE-II data
• Mapping the Interstellar Reddening and Extinction towards Baade's Window Using Minimum Light Colors of ab-type RR Lyrae Stars. Revelations from the De-reddened Color-Magnitude Diagrams
• Selecting asteroids for a targeted spectroscopic survey
• ANTARES: Progress towards building a `Broker' of time-domain alerts
• Chromospherically Active Stars in the Galactic Bulge

Cited by in corpus (13)

• The ANTARES Astronomical Time-Domain Event Broker
• Anomaly detection in the Zwicky Transient Facility DR3
• A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
• Real-Time Detection of Anomalies in Large-Scale Transient Surveys
• Inferencing Progenitor and Explosion Properties of Evolving Core-collapse Supernovae from Zwicky Transient Facility Light Curves
• Preparing to discover the unknown with Rubin LSST -- I: Time domain
• Deep Drilling in the Time Domain with DECam: Survey Characterization
• A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients
• Using Gaussian Processes to detect AGN flares
• AT 2020iko: a WZ Sge-type DN candidate with an anomalous precursor event
• Anomaly Detection to identify Transients in LSST Time Series Data
• Learning novel representations of variable sources from multi-modal $\text{Gaia}$ data via autoencoders
• The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection