发表机构
The Kavli Institute for Cosmology (KICC), University of Cambridge; Cavendish Laboratory, University of Cambridge（剑桥大学卡弗里宇宙学研究所; 剑桥大学卡文迪许实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CERIDWEN是一个基于JAX的GPU原生SED拟合框架，通过端到端可微模型和并行嵌套采样，实现全贝叶斯恒星族推断，速度比CPU快134倍，支持非参数SFH和[α/Fe]联合采样。
AI 中文摘要
JWST增加了具有高质量光谱能量分布（SEDs）的高红移星系数量及其信息含量。与此同时，来自Euclid、Rubin的LSST和Roman的广域巡天将使星系样本增加几个数量级。分析这些数据集需要既灵活又计算高效的恒星族模型。我们提出CERIDWEN，一个用JAX编写的GPU原生SED拟合框架，具有端到端可微的前向模型，涵盖恒星族、星云发射、尘埃消光和发射，以及投影到观测者参考系。其向量化、编译的架构使得嵌套采样能够在GPU上并行替换一批活动点，这使得灵活的恒星族模型在全贝叶斯推断下变得可行。自动微分还为包中的基于梯度的采样器提供精确梯度。我们联合推断时间依赖的化学增丰历史，而不是单一恒星金属丰度，并展示了具有约120个年龄区间的非参数恒星形成历史（SFHs）。使用来自FSPS的α增强恒星库，CERIDWEN可以联合采样恒星[α/Fe]与[Fe/H]、质量和SFH，使得联合后验明确表示[Fe/H]-[α/Fe]简并。在受控模拟中，CERIDWEN以校准良好的后验不确定性恢复参数，而对真实JWST观测的拟合再现了已建立的Prospector框架的后验：在单个GPU上，CERIDWEN完成一次拟合的中位采样时间约为4分钟，每次拟合比等效的基于CPU的Prospector运行快约134倍。因此，CERIDWEN使全贝叶斯推断对更大的星系样本和更灵活的恒星族模型变得实用，减少了SED拟合中探索物理复杂性的计算限制。
英文摘要
JWST has increased both the number of high-redshift galaxies with high-quality spectral energy distributions (SEDs) and their information content. In parallel, wide-area surveys from Euclid, Rubin's LSST, and Roman will increase galaxy samples by orders of magnitude. Analysing these datasets requires stellar-population models that are both flexible and computationally efficient. We present CERIDWEN, a GPU-native SED fitting framework written in JAX, with an end-to-end differentiable forward model spanning stellar populations, nebular emission, dust attenuation and emission, and projection into the observer frame. Its vectorised, compiled architecture lets nested sampling replace a batch of live points in parallel on the GPU, which makes flexible stellar-population models tractable under full Bayesian inference. Automatic differentiation also provides exact gradients for the gradient-based samplers in the package. We jointly infer time-dependent chemical-enrichment histories instead of a single stellar metallicity, and demonstrate non-parametric star-formation histories (SFHs) with $\sim$120 age bins. Using $α$-enhanced stellar libraries from FSPS, CERIDWEN can sample stellar [$α$/Fe] jointly with [Fe/H], mass, and SFH, so that the joint posterior represents the [Fe/H]-[$α$/Fe] degeneracy explicitly. In controlled mocks, CERIDWEN recovers parameters with well-calibrated posterior uncertainties, while fits to real JWST observations reproduce posteriors from the established Prospector framework: on a single GPU, CERIDWEN completes a fit in a median sampling time of $\sim$4 min, $\sim$134$\times$ faster per fit than equivalent CPU-based Prospector runs. CERIDWEN therefore makes full Bayesian inference practical for larger galaxy samples and more flexible stellar-population models, reducing computational constraints on the physical complexity explored in SED fitting.
Comments25 pages, 18 figures, 2 tables, plus appendices. Submitted to MNRAS. The package is available at https://github.com/Espe13/ceridwen and via pip install ceridwen
