2025ApJ...978L..14H

Traditional methods for determining the radius of a 1.4 M_⊙ neutron star (R_1.4) rely on specific equation-of-state (EOS) models that describe various types of dense nuclear matter. This dependence on EOS models can introduce substantial systematic uncertainties, which may exceed the measurement uncertainties when constraining R_1.4. In this study, we explore a novel approach to constraining R_1.4 using data from Neutron Star Interior Composition Explorer observations of PSR J0030+0451 (J0030) and PSR J0437-4715 (J0437). However, this work presents a more data-driven analysis framework, substantially decreasing the need for EOS assumptions. By analyzing the mass-radius measurements of these two neutron stars, we infer R_1.4 using statistical methods based mostly on observational data. We examine various hotspot configurations for J0030, along with new J0437 observations, and their effects on the inferred radius. Our results are consistent with X-ray timing, gravitational-wave, and nuclear physics constraints, while avoiding EOS-related biases. The same method has also been applied to a simulated mass-radius data set, based on our knowledge of future X-ray telescopes, demonstrating the model's ability to recover the injected R_1.4 value in certain cases. This method provides a data-driven pathway for extracting neutron star properties and offers a new approach for future observational efforts in neutron star astrophysics.