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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