Uncertain classification of Variable Stars: handling observational GAPS and noise

TL;DR

This paper introduces a novel probabilistic approach using Gaussian Processes and bootstrapping to improve variable star classification accuracy with limited observational data, addressing gaps and noise in lightcurves.

Contribution

It presents a new method that incorporates measurement uncertainty into feature extraction, enhancing early classification of variable stars from incomplete lightcurves.

Findings

Achieves around 80% accuracy with only 5% of observations for RR Lyrae stars.

Demonstrates improved classification performance on MACHO and OGLE catalogs.

Highlights the importance of considering feature errors due to observational gaps.

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.