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

TL;DR

This paper introduces a novel framework combining simulation-based inference and universal relations to improve the precision and accuracy of neutron star property predictions, effectively handling uncertainties from the nuclear equation of state.

Contribution

The work develops a new approach that integrates SBI with universal relations, enabling systematic exploration, calibration, and uncertainty quantification in neutron star modeling.

Findings

A new universal relation for neutron star radius as a function of mass and modes.

SBI surpasses traditional methods in predictive power and uncertainty estimation.

Calibration of universal relations reduces systematic errors effectively.

Abstract

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.