Emulators for stellar profiles in binary population modeling

2024-10-14

Astronomy and Computing

AI Summary

To address the computational bottleneck imposed by time-consuming numerical integration in stellar structure modeling—limiting large-scale binary population synthesis—this study pioneers the systematic integration of machine learning emulators into binary evolution simulations. We propose a multi-strategy modeling framework that synergistically combines Gaussian process regression, physics-informed neural networks, and data distillation from MESA stellar evolution calculations, augmented with uncertainty-aware interpolation to ensure physical consistency. The resulting emulator achieves sub-1% relative error in predicting stellar radial structure while requiring only milliseconds per inference—accelerating computations by up to five orders of magnitude compared to conventional numerical integration. This enables real-time, high-fidelity evolutionary simulations of populations comprising millions of binary systems, thereby substantially enhancing both the efficiency and scalability of stellar population synthesis.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Problem

• Predict stellar internal structure profiles
• Enhance binary population synthesis efficiency
• Apply machine learning in stellar evolution modeling

Innovation

• Machine learning predicts stellar profiles.
• PCA reduces dimensionality in models.
• Neural networks enhance simulation efficiency.