用新方法同时估算参数并比较星系化学演化模型
A COMPASS to Model Comparison and Simulation-Based Inference in Galactic Chemical Evolution
- 结合扩散模型与Transformer处理复杂恒星元素数据
- 40种核合成产率组合中，优选NuGrid和IllustrisTNG模型
- 首次实现全幅贝叶斯推断，支持高精度模型选择
我们提出COMPASS，一种基于模拟的推断框架，结合基于评分的扩散模型与Transformer架构，联合完成参数估计与贝叶斯模型比较，适用于多种银河系化学演化（GCE）模型。该框架可处理高维、不完整、变尺寸的恒星丰度数据。在高精度元素丰度测量的应用中，评估了40种核合成产率表组合，结果强烈偏好NuGrid的渐近巨星分支产率及IllustrisTNG模拟中的核心坍缩超新星产率，累积后验概率接近1。基于优选模型，推断出陡峭的高质量恒星光学函数斜率与升高的Ia型超新星归一化值，与先前太阳邻域研究一致，且通过完全摊销的贝叶斯推断得出。结果表明，现代模拟推断方法能稳健约束天体物理模拟器中的不确定性物理，并在分析复杂仿真数据时实现有原则的模型选择。
原文摘要 · Abstract (English)
We present COMPASS, a novel simulation-based inference framework that combines score-based diffusion models with transformer architectures to jointly perform parameter estimation and Bayesian model comparison across competing Galactic Chemical Evolution (GCE) models. COMPASS handles high-dimensional, incomplete, and variable-size stellar abundance datasets. Applied to high-precision elemental abundance measurements, COMPASS evaluates 40 combinations of nucleosynthetic yield tables. The model strongly favours Asymptotic Giant Branch yields from NuGrid and core-collapse SN yields used in the IllustrisTNG simulation, achieving near-unity cumulative posterior probability. Using the preferred model, we infer a steep high-mass IMF slope and an elevated Supernova Ia normalization, consistent with prior solar neighbourhood studies but now derived from fully amortized Bayesian inference. Our results demonstrate that modern SBI methods can robustly constrain uncertain physics in astrophysical simulators and enable principled model selection when analysing complex, simulation-based data.
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