Accurate X-ray Timing in the Presence of Systematic Biases With Simulation-Based Inference. (arXiv:2104.03278v2 [astro-ph.IM] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Huppenkothen_D/0/1/0/all/0/1">D. Huppenkothen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bachetti_M/0/1/0/all/0/1">M. Bachetti</a>

Because many of our X-ray telescopes are optimized towards observing faint
sources, observations of bright sources like X-ray binaries in outburst are
often affected by instrumental biases. These effects include dead time and
photon pile-up, which can dramatically change the statistical inference of
physical parameters from these observations. While dead time is difficult to
take into account in a statistically consistent manner, simulating dead
time-affected data is often straightforward. This structure makes the issue of
inferring physical properties from dead time-affected observations fall into a
class of problems common across many scientific disciplines. There is a growing
number of methods to address them under the name of Simulation-Based Inference
(SBI), aided by new developments in density estimation and statistical machine
learning. In this paper, we introduce SBI as a principled way to infer
variability properties from dead time-affected light curves. We use Sequential
Neural Posterior Estimation to estimate the posterior probability for
variability properties. We show that this method can recover variability
parameters on simulated data even when dead time is variable, and present
results of an application of this approach to NuSTAR observations of the
galactic black hole X-ray binary GRS 1915+105.

Because many of our X-ray telescopes are optimized towards observing faint
sources, observations of bright sources like X-ray binaries in outburst are
often affected by instrumental biases. These effects include dead time and
photon pile-up, which can dramatically change the statistical inference of
physical parameters from these observations. While dead time is difficult to
take into account in a statistically consistent manner, simulating dead
time-affected data is often straightforward. This structure makes the issue of
inferring physical properties from dead time-affected observations fall into a
class of problems common across many scientific disciplines. There is a growing
number of methods to address them under the name of Simulation-Based Inference
(SBI), aided by new developments in density estimation and statistical machine
learning. In this paper, we introduce SBI as a principled way to infer
variability properties from dead time-affected light curves. We use Sequential
Neural Posterior Estimation to estimate the posterior probability for
variability properties. We show that this method can recover variability
parameters on simulated data even when dead time is variable, and present
results of an application of this approach to NuSTAR observations of the
galactic black hole X-ray binary GRS 1915+105.

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