Reconstruction of fast-rotating neutron star observables with the neural network

TL;DR

This paper introduces a neural network approach to rapidly and accurately reconstruct neutron star observables affected by rotation, significantly speeding up traditional computational methods.

Contribution

The authors develop causal convolutional neural networks that efficiently model rotating neutron star properties, enabling fast inference compared to traditional methods.

Findings

Neural networks accurately reproduce RNS results for various EoS.

The trained networks evaluate neutron star configurations in about 50ms.

Speedup over traditional RNS calculations is approximately 36,000 times.

Abstract

Rotation can significantly affect neutron-star (NS) properties, but accurate modeling of rapidly rotating NSs requires solving a two-dimensional, axially symmetric system, making traditional calculations too expensive for inference analyses that demand a large amount of model evaluations. We develop a causal convolutional neural networks that preserve the chronological-like dependence of NS properties on the equation of state (EoS) and rapidly reconstruct observables for static, Keplerian, and rotating configurations. Using \texttt{RNS}, we generate a dataset of NS observables and use it to train our networks. We validate our networks with three representative EoS (SFHo, SLy4, and DD2) and find that the they accurately reproduce the \texttt{RNS} results. The trained networks evaluate NS configurations for a single EoS in $\sim 50$ms, providing a substantial speedup over typical \texttt{RNS} runtimes of $\sim 30$ min and enabling efficient inference analyses involving rapidly rotating NSs.