Uncertain classification of Variable Stars: handling observational GAPS and noise

Abstract

Automatic classification methods applied to sky surveys have revolutionized the astronomical target selection process. Most surveys generate a vast amount of time series, or \quotes{lightcurves}, that represent the brightness variability of stellar objects in time. Unfortunately, lightcurves' observations take several years to be completed, producing truncated time series that generally remain without the application of automatic classifiers until they are finished. This happens because state of the art methods rely on a variety of statistical descriptors or features that present an increasing degree of dispersion when the number of observations decreases, which reduces their precision. In this paper we propose a novel method that increases the performance of automatic classifiers of variable stars by incorporating the deviations that scarcity of observations produces. Our method uses Gaussian Process Regression to form a probabilistic model of each lightcurve's observations. Then, based on this model, bootstrapped samples of the time series features are generated. Finally a bagging approach is used to improve the overall performance of the classification. We perform tests on the MACHO and OGLE catalogs, results show that our method classifies effectively some variability classes using a small fraction of the original observations. For example, we found that RR Lyrae stars can be classified with around 80\% of accuracy just by observing the first 5\% of the whole lightcurves' observations in MACHO and OGLE catalogs. We believe these results prove that, when studying lightcurves, it is important to consider the features' error and how the measurement process impacts it.

References in corpus (6)

• Automated supervised classification of variable stars I. Methodology
• The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database
• Supervised detection of anomalous light-curves in massive astronomical catalogs
• De-Trending Time Series for Astronomical Variability Surveys
• An improved quasar detection method in EROS-2 and MACHO LMC datasets
• The Optical Gravitational Lensing Experiment. OGLE-III Photometric Maps of the Large Magellanic Cloud

Cited by in corpus (8)

• Autoregressive Times Series Methods for Time Domain Astronomy
• Stellar population of the Rosette Nebula and NGC 2244: application of the probabilistic random forest
• The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves
• Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way
• Semi-Supervised Classification and Clustering Analysis for Variable Stars
• Informative Bayesian model selection for RR Lyrae star classifiers
• An Information Theory Approach on Deciding Spectroscopic Follow Ups
• Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift