Emulators for stellar profiles in binary population modeling

• URL: http://arxiv.org/abs/2410.11105v1
• Date: Mon, 14 Oct 2024 21:33:10 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: 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 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 delivering more information about the evolution of stellar internal structure, these emulators will enable faster simulations of higher physical fidelity with large-scale simulations of binary star population synthesis possible with POSYDON and other population synthesis codes.

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