Bayesian Inference of Neutron Star Properties in $f(Q)$ Gravity Using NICER Observations
Sneha Pradhan, N. K. Patra, Kai Zhou, and P. K. Sahoo

TL;DR
This study performs Bayesian inference on neutron star properties within $f(Q)$ gravity using NICER data, finding the exponential model most consistent with observations and predicting maximum masses that extend into the lower mass gap.
Contribution
First Bayesian analysis of neutron stars in $f(Q)$ gravity confronting NICER data, identifying the exponential model as statistically preferred and exploring implications for neutron star mass predictions.
Findings
Exponential $f(Q)$ model is statistically favored over linear and logarithmic models.
Predicted neutron star radius at 1.4 solar masses is approximately 11.27 km.
Maximum neutron star mass reaches about 2.98 solar masses, extending into the lower mass gap.
Abstract
In this work, we investigate neutron stars (NSs) in the strong field regime within the framework of symmetric teleparallel gravity, considering three representative models: linear, logarithmic, and exponential. While Bayesian studies of NS observations are well established in general relativity and curvature based modified gravity theories, such analyses in gravity remain largely unexplored. For the first time we perform a Bayesian inference analysis by confronting theoretical NS mass-radius predictions with NICER observations of PSR J0030+0451, PSR J0740+6620, PSR J0437+4715, and PSR J0614+3329 in the background of nonmetricity based gravity. The dense matter equation of state is fixed to DDME2 in order to isolate the effects of modified gravity on NS structure. Our results show that the exponential model is statistically preferred over the linear and logarithmic…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Cosmology and Gravitation Theories · Astrophysical Phenomena and Observations
