In the past few years, NICER data has enabled mass and radius inferences for various pulsars, and thus shed light on the equation of state for dense nuclear matter. This is achieved through a technique called pulse profile modeling. The importance of the results necessitates careful validation and testing of the robustness of the inference procedure. In this paper, we investigate the effect of sampler choice for x-psi (x-ray pulse simulation and inference), an open-source package for pulse profile modeling and Bayesian statistical inference that has been used extensively for analysis of NICER data. We focus on the specific case of the high-mass pulsar PSR $\mathrm{J}0740+6620$. Using synthetic data that mimics the most recently analyzed NICER and XMM-Newton datasets of PSR $\mathrm{J}0740+6620$, we evaluate the parameter recovery performance, convergence, and computational cost for multinest’s multimodal nested sampling algorithm and ultranest’s slice nested sampling algorithm. We find that both samplers perform reliably, producing accurate and unbiased parameter estimation results when analyzing simulated data. We also investigate the consequences for inference using the real data for PSR $\mathrm{J}0740+6620$, finding that both samplers produce consistent credible intervals.

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• Equations of state of nuclear matter
• Neutron stars & pulsars
• Statistical methods