用机器学习快速预测双星内部结构，提升模拟效率。
Emulators for stellar profiles in binary population modeling
- 用主成分分析降维+神经网络预测恒星径向结构
- 精度接近最近邻法，内存占用大幅降低
- 适合大规模双星演化模拟研究者使用
恒星内部结构知识对理解其演化至关重要。新型双星合成代码POSYDON包含一个模块，基于预先计算的模型集，对双星系统在MESA演化结束时的恒星与双星属性进行插值。本文提出一种新的机器学习代理方法，用于预测恒星剖面（即沿径向的内部结构）。采用主成分分析实现降维，并使用全连接前馈神经网络进行预测。结果表明，该方法精度与最近邻近似相当，但在内存和存储效率方面具有显著优势。所提出的代理框架为建模恒星内部结构提供了灵活性，将推动更高物理保真度的快速模拟，为广泛的恒星与双星演化大规模群体研究奠定基础。
原文摘要 · Abstract (English)
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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