We present new constraints on the neutron star equation of state (EOS) and mass distribution using a unified Bayesian inference framework that incorporates the latest NICER measurements, including PSR J0614-3329, alongside gravitational-wave data, radio pulsar masses, and nuclear theory. By systematically comparing four inference scenarios—varying in the inclusion of PSR J0614-3329 and in the pulse profile model used for PSR $\mathrm{J}0030+0451$—we quantify the impact of observational and modeling choices on dense matter inference. We find that systematics from hotspot geometry dominate EOS uncertainties: the choice of hotspot geometry for PSR $\mathrm{J}0030+0451$ leads to significant shifts in the inferred stiffness of the EOS and maximum neutron star mass. In contrast, PSR J0614-3329 mildly softens the EOS at low densities, reducing the radius at $1.4M_{\odot }$ by $\sim 100\mathrm{m}$. A Bayesian model comparison yields a Bayes factor of ${\log }_{10}\mathrm{BF}\approx 1.58$ in favor of the $\mathrm{ST}+\mathrm{PDT}$ hotspot model over PDT-U, providing strong evidence that multimessenger EOS inference can statistically discriminate between competing NICER pulse profile models. These results highlight the critical role of NICER systematics in dense matter inference and the power of joint analyses in breaking modeling degeneracies.

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• General relativity
• Gravitational waves
• X ray astronomy
• Neutron stars & pulsars
• Astronomical masses & mass distributions
• Numerical simulations in gravitation & astrophysics