Emulators for stellar profiles in binary population modeling

• URL: http://arxiv.org/abs/2410.11105v2
• Date: Tue, 11 Feb 2025 20:18:12 GMT
• Title: Emulators for stellar profiles in binary population modeling
• Authors: Elizabeth Teng, Ugur Demir, Zoheyr Doctor, Philipp M. Srivastava, Shamal Lalvani, Vicky Kalogera, Aggelos Katsaggelos, Jeff J. Andrews, Simone S. Bavera, Max M. Briel, Seth Gossage, Konstantinos Kovlakas, Matthias U. Kruckow, Kyle Akira Rocha, Meng Sun, Zepei Xing, Emmanouil Zapartas,
• Abstract summary: In this work, we present a new emulation method for predicting stellar profiles, i.e., the internal stellar structure along the radial axis. 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.
• Score: 0.5430323214461458
• License:
• 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.

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