用于精确星震学的恒星演化机器学习模拟器基准测试
Benchmarking Machine Learning Emulators of Stellar Evolution for Precision Asteroseismology
浏览论文内容
中文总结 AI 辅助
本研究测试多种ML算法在恒星演化模拟中的表现，发现神经网络更优，稀疏网格有缺陷，建议采用自适应网格，且神经网络集成可更准确反映模拟器可靠性，为精确星震学提供基准。
中文摘要 AI 辅助
快速且精确的恒星演化模拟器（ surrogate models，即采用机器学习（ML）近似昂贵模拟输出的替代模型）是现代恒星表征、分层推断及种群合成的强大工具。我们通过对质量M=[0.7,1.2]太阳质量的主序星模型训练ML算法，分析可靠模拟所需的网格密度。该质量范围因辐射核心到对流核心转变导致的快速演化行为，以及需匹配NASA开普勒任务为这类恒星提供的千分之一精度的地震数据，成为模拟的难点。我们从解析模型及MESA、YREC、MIST、ASTEC生成网格，对比线性插值、k近邻、随机森林、神经网络（NNs）在插值恒星观测量（有效温度T_eff、光度L、大频率间隔Delta nu、最大频率nu_max）的表现。尽管NNs优于其他方法，但稀疏网格在核心转变区域引发局部失效，导致导数不稳定、集成结果不一致及推断过程中后验分布碎片化。密集网格的性能增益不均，表明自适应网格生成应优于均匀细化。最后，我们证明NN集成可实现局部不确定性传播，比全局不确定性估计更准确地反映模拟器在参数空间的可靠性。由于我们仅考虑主序星上仅变化恒星质量和年龄的二维情况，这些结果代表了精确星震学恒星演化模拟挑战的下限。
英文摘要
Fast and accurate stellar evolution emulators---surrogate models that approximate expensive simulation outputs with machine learning (ML)---are powerful tools for modern stellar characterization, hierarchical inference, and population synthesis. We analyze the grid density required for reliable emulation by training ML algorithms on main-sequence models with masses M=[0.7,1.2] solar masses. This range is challenging to emulate due to rapidly varying evolutionary behavior caused by the radiative-to-convective core transition, as well as the requirement to match the part-per-thousand seismic precision that has been delivered for such stars from the NASA Kepler mission. Generating grids from analytical models, as well as MESA, YREC, MIST, and ASTEC, we compare linear interpolation, k-nearest neighbors, random forests, and neural networks (NNs) in interpolating the stellar observables: T_eff, L, Delta nu, and nu_max. While NNs outperform other methods, sparse grids induce localized failures in the core-transition region, resulting in unstable derivatives, ensemble disagreement, and fragmented posterior distributions during inference. Performance gains from denser grids are non-uniform, suggesting that adaptive grid generation should be favored over uniform refinement. Finally, we show that NN ensembles allow for localized uncertainty propagation, more accurately reflecting emulator reliability across parameter space than global uncertainty estimates. As we consider only the two-dimensional case of varying only stellar mass and age along the main sequence, these results represent a lower bound on the challenge in emulating stellar evolution simulations for precision asteroseismology.
发表机构
- Yale University（耶鲁大学）
- Institute for Foundations of Data Science, Yale University（耶鲁大学数据科学基础研究所）
机构由 AI 辅助整理，请以论文原文为准。
