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Using an explicitly isospin-dependent parametric Equation of State (EOS) forthe core of neutron stars (NSs) within the Bayesian statistical approach, weinfer the EOS parameters of super-dense neutron-rich nuclear matter from threesets of imagined mass-radius correlation data representing typical predictionsby various nuclear many-body theories, i.e, the radius stays the same,decreases or increases with increasing NS mass within $\pm 15\%$ between 1.4M$_{\odot}$ and 2.0 M$_{\odot}$. The corresponding average density increasesquickly, slowly or slightly decreases as the NS mass increases from 1.4M$_{\odot}$ to 2.0 M$_{\odot}$. Using the posterior probability distributionfunctions (PDFs) of EOS parameters inferred from GW170817 and NICER radius datafor canonical NSs as references, we investigate how future radius measurementsof massive NS will improve our knowledge about the EOS of super-denseneutron-rich nuclear matter, especially its symmetry energy term, compared towhat people have already learned from analyzing the GW170817 and NICER data.While the EOS of symmetric nuclear matter (SNM) inferred from the three datasets are approximately the same, the corresponding high-density symmetryenergies at densities above about $2\rho_0$ are very different, indicating thatthe radii of massive NSs carry reliable information about the high-densitybehavior of nuclear symmetry energy with little influence from the remaininguncertainties of the SNM EOS.

Bayesian Inference of the Symmetry Energy of Super-Dense Neutron-Rich Matter from Future Radius Measurements of Massive Neutron Stars