Combining simulation-based inference and universal relations for precise and accurate neutron star science

In this work, we propose a novel approach for identifying, constructing, and validating precise and accurate universal relations for neutron star bulk quantities. A central element is simulation-based inference (SBI), which we adopt to treat uncertainties due to the unknown nuclear equation of state (EOS) as intrinsic non-trivial noise. By assembling a large set of bulk properties of non-rotating neutron stars across multiple state-of-the-art EOS models, we are able to systematically explore universal relations in high-dimensional parameter spaces. Our framework further identifies the most promising parameter combinations, enabling a more focused and traditional construction of explicit universal relations. At the same time, SBI does not rely on explicit relations; instead, it directly provides predictive distributions together with a quantitative measure of systematic uncertainties, which are not captured by conventional approaches. As an example, we report a new universal relation that allows us to obtain the radius as a function of mass, fundamental mode, and one pressure mode. Our analysis shows that SBI can surpass the predictive power of this universal relation while also mitigating systematic errors. Finally, we demonstrate how universal relations can be further calibrated to mitigate systematic errors accurately.