# Emulators for stellar profiles in binary population modeling

arXiv: https://arxiv.org/abs/2410.11105 · HTML: https://mlsanity.com/paper/2410.11105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses](https://mlsanity.com/paper/2009.03323/) (2020)
- [Fundamental Parameters of Main-Sequence Stars in an Instant with Machine Learning](https://mlsanity.com/paper/1607.02137/) (2016)
- [Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations](https://mlsanity.com/paper/2203.16683/) (2022)
- [Flow-Based Generative Emulation of Grids of Stellar Evolutionary Models](https://mlsanity.com/paper/2407.09427/) (2024)

## Abstract (arXiv)

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.