Emulators for stellar profiles in binary population modeling

TL;DR

This paper introduces a machine learning-based emulation method for predicting stellar internal profiles, enhancing the efficiency and scalability of binary population synthesis models like POSYDON.

Contribution

It presents a novel neural network approach combined with PCA for fast, accurate stellar profile predictions, improving upon traditional methods in efficiency and storage.

Findings

Accuracy comparable to nearest neighbor methods

Significant improvements in memory and storage efficiency

Enables faster, high-fidelity population simulations

Abstract

Knowledge about the internal physical structure of stars is crucial to understanding their evolution. The novel binary population synthesis code POSYDON includes a module for interpolating the stellar and binary properties of any system at the end of binary MESA evolution based on a pre-computed set of models. In this work, we present a new emulation method for predicting stellar profiles, i.e., the internal stellar structure along the radial axis, using machine learning techniques. We use principal component analysis for dimensionality reduction and fully-connected feed-forward neural networks for making predictions. We find accuracy to be comparable to that of nearest neighbor approximation, with a strong advantage in terms of memory and storage efficiency. By providing a versatile framework for modeling stellar internal structure, the emulation method presented here will enable faster simulations of higher physical fidelity, offering a foundation for a wide range of large-scale population studies of stellar and binary evolution.