Systematic evaluation of variability detection methods for eROSITA

06/28/2021 ∙ by Johannes Buchner, et al. ∙ 0

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.



There are no comments yet.


page 8

Code Repositories


Bayesian excess variance for Poisson data time series with backgrounds.

view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.