达特茅斯恒星演化模拟器（DSEE）1：生成式恒星演化模型数据库
Dartmouth Stellar Evolution Emulator (DSEE) 1: Generative Stellar Evolution Model Database
AI总结：
DSEE基于流式模型构建了包含八百万条演化轨迹的数据库，实现了高维物理条件下的连续插值和概率预测，显著提升了恒星演化建模的效率和精度。
摘要：
我们提出了达特茅斯恒星演化模拟器（DSEE），这是一个基于流的恒星演化模型模拟器，它在包含超过八百萬条演化轨迹的综合性数据库上进行训练，这些轨迹在二十个输入物理维度上变化，并覆盖了质量和成分的广泛范围。DSEE学习相条件下的恒星状态快照，将轨迹和等时线构造统一为生成模型的边缘。它能够实现高维物理条件下的连续插值，提供具有校准的可信区间概率预测，并在直接建模上实现数量级的加速。对当前恒星演化模型的验证显示，在H-R图上具有高保真度，而分布测试能够恢复从暴力蒙特卡洛采样得到的完整分布。为了扩大影响，DSEE被整合到开源的CONF1DENCE包中，使快速、端到端的恒星轨迹和等时线创建成为可能。CONF1DENCE包括能够考虑观测效应的不确定性意识的年龄确定能力。CONF1DENCE用生成式、物理边缘化的模拟器取代了定制的固定物理网格，为恒星建模设定了新的实用标准，并使大规模调查分析在严谨的不确定性下成为可能。
英文摘要：
We present the Dartmouth Stellar Evolution Emulator (DSEE), a flow-based stellar evolution model emulator trained on a comprehensive database comprising over eight million evolutionary tracks that vary across twenty input-physics dimensions and span broad ranges in mass and composition. DSEE learns phase-conditioned stellar state snapshots, unifying track and isochrone construction as marginals of one generative model. It delivers continuous interpolation across high-dimensional physics, probabilistic predictions with calibrated credible intervals, and orders-of-magnitude speedups over direct modeling. Validation against current stellar evolution models shows high fidelity across the HR diagrams, while distributional tests recover the full distributions obtained from brute-force Monte Carlo sampling. To broaden impact, DSEE is integrated into the open-source CONF1DENCE package, enabling fast, end-to-end creation of stellar tracks and isochrones. CONF1DENCE includes the ability to make uncertainty-aware age determinations for clusters taking into account observational effects. CONF1DENCE replaces bespoke, fixed-physics grids with a generative, physics-marginalized emulator, setting a practical new standard for stellar modeling and enabling survey-scale analyses with rigorous uncertainty.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
