X-PSI Parameter Recovery for Temperature Map Configurations Inspired by PSR J0030+0451

TL;DR

This study evaluates the reliability of the X-PSI pipeline in recovering neutron star parameters from synthetic data mimicking NICER observations of PSR J0030+0451, highlighting its robustness and vulnerabilities.

Contribution

It provides a comprehensive assessment of X-PSI's performance and sensitivity, revealing key factors affecting parameter recovery and suggesting convergence testing as a solution.

Findings

X-PSI can recover true mass and radius within credible intervals.

Noise significantly affects parameter estimation accuracy.

Multi-modal posteriors indicate potential biases without thorough exploration.

Abstract

In the last few years, the NICER collaboration has provided mass and radius inferences, via pulse profile modeling, for two pulsars: PSR J0030+0451 and PSR J0740+6620. Given the importance of these results for constraining the equation of state of dense nuclear matter, it is crucial to validate them and test their robustness. We therefore explore the reliability of these results and their sensitivity to analysis settings and random processes, including noise, focusing on the specific case of PSR J0030+0451. We use X-PSI, one of the two main analysis pipelines currently employed by the NICER collaboration for mass and radius inferences. With synthetic data that mimic the PSR J0030+0451 NICER data set, we evaluate the recovery performances of X-PSI under conditions never tested before, including complex modeling of the thermally emitting neutron star surface. For the test cases explored, our results suggest that X-PSI is capable of recovering the true mass and radius within reasonable credible intervals. This work also reveals the main vulnerabilities of the analysis: a significant dependence on noise and the presence of multi-modal structure in the posterior surface. Noise particularly impacts our sensitivity to the analysis settings and widths of the posterior distributions. The multi-modal structure in the posterior suggests that biases could be present if the analysis is unable to exhaustively explore the parameter space. Convergence testing, to ensure an adequate coverage of the parameter space and a suitable representation of the posterior distribution, is one possible solution to these challenges.