Lightcurve Classification in Massive Variability Surveys

Abstract

This paper pioneers the use of neural networks to provide a fast and automatic way to classify lightcurves in massive photometric datasets. As an example, we provide a working neural network that can distinguish microlensing lightcurves from other forms of variability, such as eruptive, pulsating, cataclysmic and eclipsing variable stars. The network has five input neurons, a hidden layer of five neurons and one output neuron. The five input variables for the network are extracted by spectral analysis from the lightcurve datapoints and are optimised for the identification of a single, symmetric, microlensing bump. The output of the network is the posterior probability of microlensing. The committee of neural networks successfully passes tests on noisy data taken by the MACHO collaboration. When used to process 5000 lightcurves on a typical tile towards the bulge, the network cleanly identifies the single microlensing event. When fed with a sub-sample of 36 lightcurves identified by the MACHO collaboration as microlensing, the network corroborates this verdict in the case of 27 events, but classifies the remaining 9 events as other forms of variability. For some of these discrepant events, it looks as though there are secondary bumps or the bump is noisy or not properly contained. Neural networks naturally allow for the possibility of novelty detection -- that is, new or unexpected phenomena which we may want to follow up. The advantages of neural networks for microlensing rate calculations, as well as the future developments of massive variability surveys, are both briefly discussed.

14 pages, 8 figures, version in press at MNRAS

References in corpus (1)

• A Candidate M31/M32 Intergalactic Microlensing Event

Cited by in corpus (30)

• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• Mirror particles and mirror matter: 50 years of speculation and search
• The Discovery of Tidal Tails Around NGC 5466
• Random forest automated supervised classification of Hipparcos periodic variable stars
• Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
• Selection of radio pulsar candidates using artificial neural networks
• OGLE-III Microlensing Events and the Structure of the Galactic Bulge
• Finding outlier light-curves in catalogs of periodic variable stars
• The OGLE View of Microlensing towards the Magellanic Clouds. I. A Trickle of Events in the OGLE-II LMC data
• Data Mining for Gravitationally Lensed Quasars
• Automated classification of variable stars for ASAS 1-2 data
• Microlensing as a probe of the Galactic structure; 20 years of microlensing optical depth studies
• Deep multi-survey classification of variable stars
• Deep Learning for Image Sequence Classification of Astronomical Events
• Lightcurve Classification in Massive Variability Surveys II: Transients towards the Large Magellanic Cloud
• Detecting Variability in Massive Astronomical Time-Series Data I: application of an infinite Gaussian mixture model
• Gravitational Microlensing I: A Unique Astrophysical Tool
• Comparing Multi-class, Binary and Hierarchical Machine Learning Classification schemes for variable stars
• Meta Classification for Variable Stars
• Automatic classification of eclipsing binaries light curves using neural networks
• A microlensing search of 700 million VVV light curves
• Contamination in the MACHO dataset and the puzzle of LMC Microlensing
• Probing MACHOs by observation of M31 pixel lensing with the 1.5m Loiano telescope
• Photometric Confirmation of MACHO Large Magellanic Cloud Microlensing Events
• The contribution of red dwarfs and white dwarfs to the halo dark matter
• Automated Detection of Classical Novae with Neural Networks
• The contribution of Oxygen-Neon white dwarfs to the MACHO content of the Galactic Halo
• Identification of the Microlens in Event MACHO-LMC-20
• A Fast Approximate Approach to Microlensing Survey Analysis
• Classifying High-cadence Microlensing Light Curves I; Defining Features