Detecting Variability in Massive Astronomical Time-Series Data I: application of an infinite Gaussian mixture model

TL;DR

This paper introduces a non-parametric Bayesian clustering framework using an infinite Gaussian mixture model to detect variable objects in large astronomical time-series datasets, effectively handling sampling biases and systematic errors.

Contribution

The paper presents a novel application of an infinite GMM with Dirichlet Process for variable object detection, accommodating complex data characteristics and reducing false positives.

Findings

Successfully applied to Northern Sky Variability Survey data

Effective in identifying variable objects with reduced false detections

Suitable for upcoming large-scale astronomical surveys

Abstract

We present a new framework to detect various types of variable objects within massive astronomical time-series data. Assuming that the dominant population of objects is non-variable, we find outliers from this population by using a non-parametric Bayesian clustering algorithm based on an infinite GaussianMixtureModel (GMM) and the Dirichlet Process. The algorithm extracts information from a given dataset, which is described by six variability indices. The GMM uses those variability indices to recover clusters that are described by six-dimensional multivariate Gaussian distributions, allowing our approach to consider the sampling pattern of time-series data, systematic biases, the number of data points for each light curve, and photometric quality. Using the Northern Sky Variability Survey data, we test our approach and prove that the infinite GMM is useful at detecting variable objects, while providing statistical inference estimation that suppresses false detection. The proposed approach will be effective in the exploration of future surveys such as GAIA, Pan-Starrs, and LSST, which will produce massive time-series data.