Assessing Universal Relations for Rapidly Rotating Neutron Stars: Insights from an Interpretable Deep Learning Perspective

TL;DR

This paper develops and interprets deep learning models to identify highly accurate, EoS-insensitive universal relations among neutron star properties, aiding in constraining the dense matter equation of state.

Contribution

It introduces novel deep neural network-based hypersurface relations for rapidly rotating neutron stars and employs SHAP for interpretability, surpassing traditional analytical methods.

Findings

Achieved within 1% accuracy for most models

Deep learning outperforms analytical expressions in complex regions

SHAP provides insights into model predictions

Abstract

Relations between stellar properties independent of the nuclear equation of state offer profound insights into neutron star physics and have practical applications in data analysis. Commonly, these relations are derived from utilizing various realistic nuclear cold hadronic, hyperonic, and hybrid EoS models, each of which should obey the current constraints and cover a wide range of stiffnesses. Concurrently, the field of multimessenger astronomy has been significantly enhanced by the advent of gravitational wave astronomy, which increasingly incorporates deep learning techniques and algorithms. At the same time, X-ray spectral data from NICER based on known pulsars are available, and additional observations are expected from upcoming missions. In this study, we revisit established universal relations, introduce new ones, and reassess them using a feed-forward neural network as a regression model. More specifically, we mainly propose ``deep'' EoS-insensitive hypersurface relations for rapidly rotating compact objects between several of the star's global parameters, which achieve an accuracy of within $1\%$ in most cases, with only a small fraction of investigated models exceeding this threshold. While analytical expressions can be used to represent some of these relations, the neural network approach demonstrates superior performance, particularly in complex regions of the parameter space. Furthermore, we use the SHapley Additive exPlanations (SHAP) method to interpret the suggested network's predictions, since is based on a strong theoretical framework inspired by the field of cooperative Game Theory. Most importantly, these highly accurate universal relations empowered with the interpretability description could be used in efforts to constrain the high-density equation of state in neutron stars, with the potential to enhance our understanding as new observables emerge.