Systematic evaluation of variability detection methods for eROSITA

TL;DR

This paper evaluates four variability detection methods for eROSITA's X-ray light curves, focusing on their false detection rates and sensitivity, to improve source variability identification in large surveys.

Contribution

It introduces a new Bayesian formulation of excess variance and systematically compares methods for variability detection in sparse, irregular X-ray data.

Findings

Amplitude maximum deviation is most sensitive for flare detection.

Bayesian methods perform best for stochastic variability.

Calibrated significance thresholds for reliable variability detection.

Abstract

The reliability of detecting source variability in sparsely and irregularly sampled X-ray light curves is investigated. This is motivated by the unprecedented survey capabilities of eROSITA onboard SRG, providing light curves for many thousand sources in its final-depth equatorial deep field survey. Four methods for detecting variability are evaluated: excess variance, amplitude maximum deviations, Bayesian blocks and a new Bayesian formulation of the excess variance. We judge the false detection rate of variability based on simulated Poisson light curves of constant sources, and calibrate significance thresholds. Simulations with flares injected favour the amplitude maximum deviation as most sensitive at low false detections. Simulations with white and red stochastic source variability favour Bayesian methods. The results are applicable also for the million sources expected in eROSITA's all-sky survey.