Explainable autoencoder for neutron star dense matter parameter estimation

TL;DR

This paper introduces a physics-informed autoencoder that encodes neutron star properties into an interpretable latent space, improving transparency and understanding of the equation of state from observational data.

Contribution

The novel autoencoder incorporates physics-based loss functions to enhance interpretability and accurately estimate neutron star EoS parameters from observational data.

Findings

Accurately estimates EoS parameters, central density, and pressure.

Provides insights into the physical connection between EoS and observable quantities.

Enhances model transparency in astrophysical data analysis.

Abstract

We present a physics-informed autoencoder designed to encode the equation of state of neutron stars into an interpretable latent space. In particular the input will be encoded in the mass, radius, and tidal deformability values of a neutron star. Unlike traditional black-box models, our approach incorporates additional loss functions to enforce explainability in the encoded representations. This method enhances the transparency of machine learning models in physics, providing a robust proof-of-concept tool to study compact stars data. Our results demonstrate that the proposed autoencoder not only accurately estimates the EoS parameters and central density/pressure but also offers insights into the physical connection between equation of state and observable physical quantities. This framework conceptualizes the physical differential equations themselves as the ``encoders", allowing interpretability of the latent space.