Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state

TL;DR

This paper introduces a novel method for uncertainty quantification in neutron star equation of state inference using machine learning, which handles arbitrary probability distributions and incorporates multimessenger observational data.

Contribution

It proposes a new approach based on Monte Carlo sampling and convolution with observational data distributions, improving reliability over previous ensemble-based methods.

Findings

The new method effectively handles non-Gaussian probability distributions.

Inclusion of multimessenger data enhances EoS inference accuracy.

Data augmentation and prior choices significantly impact results.

Abstract

We discuss the machine-learning inference and uncertainty quantification for the equation of state (EoS) of the neutron star (NS) matter directly using the NS probability distribution from the observations. We previously proposed a prescription for uncertainty quantification based on ensemble learning by evaluating output variance from independently trained models. We adopt a different principle for uncertainty quantification to confirm the reliability of our previous results. To this end, we carry out the MC sampling of data to infer an EoS and take the convolution with the probability distribution of the observational data. In this newly proposed method, we can deal with arbitrary probability distribution not relying on the Gaussian approximation. We incorporate observational data from the recent multimessenger sources including precise mass measurements and radius measurements. We also quantify the importance of data augmentation and the effects of prior dependence.