Systematic evaluation of variability detection methods for eROSITA. (arXiv:2106.14529v2 [astro-ph.HE] UPDATED)

<a href="http://arxiv.org/find/astro-ph/1/au:+Buchner_J/0/1/0/all/0/1">Johannes Buchner</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Boller_T/0/1/0/all/0/1">Thomas Boller</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bogensberger_D/0/1/0/all/0/1">David Bogensberger</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Malyali_A/0/1/0/all/0/1">Adam Malyali</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Nandra_K/0/1/0/all/0/1">Kirpal Nandra</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wilms_J/0/1/0/all/0/1">Joern Wilms</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Dwelly_T/0/1/0/all/0/1">Tom Dwelly</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Liu_T/0/1/0/all/0/1">Teng Liu</a>

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.

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.

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