New Metrics for Identifying Variables and Transients in Large Astronomical Surveys

TL;DR

This paper introduces a Gaussian process-based method for identifying variable and transient objects in large astronomical surveys, overcoming data challenges and outperforming traditional metrics.

Contribution

The authors develop a non-parametric GP regression approach that improves variability detection and interpretability in astronomical light curves without assuming specific shapes.

Findings

GP method outperforms traditional variability metrics

Approach effectively distinguishes variable sources from stable ones

Demonstrated utility in initial transient source screening

Abstract

A key science goal of large sky surveys such as those conducted by the Vera C. Rubin Observatory and precursors to the Square Kilometre Array is the identification of variable and transient objects. One approach is the statistical analysis of the time series of the changing brightness of sources, that is, their light curves. However, finding adequate statistical representations of light curves is challenging because of data quality issues such as sparsity of observations, irregular sampling, and other nuisance factors inherent in astronomical data collection. The wide diversity of objects that a large-scale survey will observe also means that making parametric assumptions about the shape of light curves is problematic. We present a Gaussian process (GP) regression approach for characterising light curve variability that addresses these challenges. Our approach makes no assumptions about the shape of a light curve and, therefore, is general enough to detect a range of variable source types. In particular, we propose using the joint distribution of GP amplitude hyperparameters to distinguish variable and transient candidates from nominally stable ones and apply this approach to 6394 radio light curves from the ThunderKAT survey. We compare our results with two variability metrics commonly used in radio astronomy, namely $\eta_\nu$ and $V_\nu$, and show that our approach has better discriminatory power and interpretability. Finally, we conduct a rudimentary search for transient sources in the ThunderKAT dataset to demonstrate how our approach might be used as an initial screening tool. Computational notebooks in Python and R are available to help facilitate the deployment of this framework to other surveys.