The Radius of the High-mass Pulsar PSR J0740+6620 with 3.6 yr of NICER Data

TL;DR

This study refines the measurements of the radius and mass of the high-mass pulsar PSR J0740+6620 using extensive NICER and XMM-Newton data, constraining neutron star equations of state.

Contribution

It provides an updated, more precise measurement of the pulsar's radius and mass, incorporating a larger data set and improved modeling techniques compared to previous analyses.

Findings

Radius: approximately 12.49 km with credible intervals

Mass: approximately 2.07 solar masses with credible intervals

Disfavors very soft equations of state for dense matter

Abstract

We report an updated analysis of the radius, mass, and heated surface regions of the massive pulsar PSR J0740+6620 using Neutron Star Interior Composition Explorer (NICER) data from 2018 September 21 to 2022 April 21, a substantial increase in data set size compared to previous analyses. Using a tight mass prior from radio timing measurements and jointly modeling the new NICER data with XMM-Newton data, the inferred equatorial radius and gravitational mass are $12.49_{-0.88}^{+1.28}$ km and $2.073_{-0.069}^{+0.069}$ $M_\odot$ respectively, each reported as the posterior credible interval bounded by the $16\,\%$ and $84\,\%$ quantiles, with an estimated systematic error $\lesssim 0.1$ km. This result was obtained using the best computationally feasible sampler settings providing a strong radius lower limit but a slightly more uncertain radius upper limit. The inferred radius interval is also close to the $R=12.76_{-1.02}^{+1.49}$ km obtained by Dittmann et al., when they require the radius to be less than $16$ km as we do. The results continue to disfavor very soft equations of state for dense matter, with $R<11.15$ km for this high-mass pulsar excluded at the $95\,\%$ probability. The results do not depend significantly on the assumed cross-calibration uncertainty between NICER and XMM-Newton. Using simulated data that resemble the actual observations, we also show that our pipeline is capable of recovering parameters for the inferred models reported in this paper.