Pulse Profile Modelling of Thermonuclear Burst Oscillations II: Handling variability

TL;DR

This paper improves neutron star parameter inference from burst oscillations by segmenting bursts to reduce variability bias, achieving ~10% uncertainties in mass and radius with high counts, though at high computational cost.

Contribution

It introduces a segmentation and joint fitting method to mitigate variability bias in pulse profile modelling of thermonuclear burst oscillations.

Findings

Segmenting bursts reduces bias in mass and radius estimates.

Joint fitting of segments achieves uncertainties within statistical limits.

Combining multiple bursts yields comparable results to single high-count bursts.

Abstract

Pulse profile modelling is a relativistic ray-tracing technique that can be used to infer masses, radii and geometric parameters of neutron stars. In a previous study, we looked at the performance of this technique when applied to thermonuclear burst oscillations from accreting neutron stars. That study showed that ignoring the variability associated with burst oscillation sources resulted in significant biases in the inferred mass and radius, particularly for the high count rates that are nominally required to obtain meaningful constraints. In this follow-on study, we show that the bias can be mitigated by slicing the bursts into shorter segments where variability can be neglected, and jointly fitting the segments. Using this approach, the systematic uncertainties on the mass and radius are brought within the range of the statistical uncertainty. With about 10$^6$ source counts, this yields uncertainties of approximately 10% for both the mass and radius. However, this modelling strategy requires substantial computational resources. We also confirm that the posterior distributions of the mass and radius obtained from multiple bursts of the same source can be merged to produce outcomes comparable to that of a single burst with an equivalent total number of counts.