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.
@article{arxiv.2106.14529,
title = {Systematic evaluation of variability detection methods for eROSITA},
author = {Johannes Buchner and Thomas Boller and David Bogensberger and Adam Malyali and Kirpal Nandra and Joern Wilms and Tom Dwelly and Teng Liu},
journal= {arXiv preprint arXiv:2106.14529},
year = {2022}
}
Comments
Variability analysis tools available https://github.com/JohannesBuchner/bexvar/. 15 min Talk: https://youtu.be/xBC1S9MTH4w. Accepted in A&A, Special Issue: The Early Data Release of eROSITA and Mikhail Pavlinsky ART-XC on the SRG Mission