A method for finding anomalous astronomical light curves and their analogs

Abstract

Our understanding of the Universe has profited from deliberate, targeted studies of known phenomena, as well as from serendipitous, unexpected discoveries, such as the discovery of a complex variability pattern in the direction of KIC 8462852 (Boyajian's star). Upcoming surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will explore the parameter space of astrophysical transients at all time scales, and offer the opportunity to discover even more extreme examples of unexpected phenomena. We investigate strategies to identify novel objects and to contextualize them within large time-series data sets in order to facilitate the discovery of new classes of objects, as well as the physical interpretation of their anomalous nature. We develop a method that combines tree-based and manifold-learning algorithms for anomaly detection in order to perform two tasks: 1) identify and rank anomalous objects in a time-domain dataset; and 2) group those anomalies according to their similarity in order to identify analogs. We achieve the latter by combining an anomaly score from a tree-based method with a dimensionality manifold-learning reduction strategy. Clustering in the reduced space allows for the successful identification of anomalies and analogs. We also assess the impact of pre-processing and feature engineering schemes and investigate the astrophysical nature of the objects that our models identify as anomalous by augmenting the Kepler data with Gaia color and luminosity information. We find that multiple models, used in combination, are a promising strategy to identify novel light curves and light curve families.

arXiv comment29 pages, 22 figures, accepted to MNRAS

References in corpus (43)

• Scikit-learn: Machine Learning in Python
• Array Programming with NumPy
• The Sloan Digital Sky Survey: Technical Summary
• Gaia Data Release 2. Summary of the contents and survey properties
• The Gaia mission
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• Understanding the Lomb-Scargle Periodogram
• The All-Sky Automated Survey for Supernovae (ASAS-SN) Light Curve Server v1.0
• Analysis of a Random Forests Model
• Physics Of Eclipsing Binaries. II. Towards the Increased Model Fidelity
• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• Nine-hour X-ray quasi-periodic eruptions from a low-mass black hole galactic nucleus
• Planet Hunters X. KIC 8462852 - Where's the Flux?
• Kepler Flares II: The Temporal Morphology of White-Light Flares on GJ 1243
• Automated supervised classification of variable stars I. Methodology
• Random forest automated supervised classification of Hipparcos periodic variable stars
• A secondary clump of red giant stars: why and where
• Finding Anomalous Periodic Time Series: An Application to Catalogs of Periodic Variable Stars
• Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
• The weirdest SDSS galaxies: results from an outlier detection algorithm
• A comparison of period finding algorithms
• Astronomaly: Personalised Active Anomaly Detection in Astronomical Data
• Variable stars across the observational HR diagram
• Pulsational Instabilities in Accreting White Dwarfs
• Anomaly detection in the Zwicky Transient Facility DR3
• Detecting outliers in astronomical images with deep generative networks
• Deep-Learnt Classification of Light Curves
• Systematic Serendipity: A Test of Unsupervised Machine Learning as a Method for Anomaly Detection
• Active Anomaly Detection for time-domain discoveries
• On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
• Detecting outliers and learning complex structures with large spectroscopic surveys - a case study with APOGEE stars
• Anomaly Detection in the Open Supernova Catalog
• Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks
• K2 Ultracool Dwarfs Survey. V. High superflare rates on rapidly rotating late-M dwarfs
• A Complete Census of Luminous Stellar Variability on Day to Decade Timescales
• K2 Ultracool Dwarfs Survey. IV. Monster flares observed on the young brown dwarf CFHT-BD-Tau 4
• Image-based Classification of Variable Stars: First Results from Optical Gravitational Lensing Experiment Data
• Active deep learning method for the discovery of objects of interest in large spectroscopic surveys
• Density Based Outlier Scoring on Kepler Data
• The Catalina Real-time Transient Survey
• Generation of a Supervised Classification Algorithm for Time-Series Variable Stars with an Application to the LINEAR Dataset
• Multivariate Time-series Analysis of Variable Objects in the Gaia Mission

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• Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework
• Supernova search with active learning in ZTF DR3
• SNAD Transient Miner: Finding Missed Transient Events in ZTF DR4 using k-D trees
• Representation Learning for Time-Domain High-Energy Astrophysics: Discovery of Extragalactic Fast X-ray Transient XRT 200515
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