用可微编程分析伽马射线数据，高效处理银河系中心谜题的复杂模型空间。
High-dimensional inference for the $γ$-ray sky with differentiable programming
- 构建可微分前向模型与似然函数，支持多形态空间分布联合推断。
- 基于变分推断在大模型空间中实现高效概率推断，适配GPU加速。
- 方法可推广至其他天体物理数据，提升分析灵活性。
我们提出利用可微概率编程技术应对天体物理伽马射线分析中庞大的模型空间问题。以长期存在的银河系中心伽马射线过剩（GCE）难题为目标，构建了可微分的前向模型和似然函数，充分利用GPU加速与向量化计算，以全概率方式同时考虑与GCE辐射一致的一系列可能空间形态。该框架支持使用变分方法在大规模模型空间中进行高效推断。本工作不仅应用于伽马射线数据，更旨在展示可微概率编程作为灵活分析天体物理数据集的强大工具。
原文摘要 · Abstract (English)
We motivate the use of differentiable probabilistic programming techniques in order to account for the large model-space inherent to astrophysical $γ$-ray analyses. Targeting the longstanding Galactic Center $γ$-ray Excess (GCE) puzzle, we construct differentiable forward model and likelihood that make liberal use of GPU acceleration and vectorization in order to simultaneously account for a continuum of possible spatial morphologies consistent with the GCE emission in a fully probabilistic manner. Our setup allows for efficient inference over the large model space using variational methods. Beyond application to $γ$-ray data, a goal of this work is to showcase how differentiable probabilistic programming can be used as a tool to enable flexible analyses of astrophysical datasets.
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