High-dimensional inference for the $\gamma$-ray sky with differentiable programming

TL;DR

This paper demonstrates how differentiable probabilistic programming can be used to efficiently analyze the complex model space of gamma-ray astrophysics, specifically addressing the Galactic Center gamma-ray Excess puzzle.

Contribution

It introduces a differentiable forward model and likelihood for gamma-ray analysis that leverages GPU acceleration, enabling flexible and efficient inference over large model spaces.

Findings

Efficient variational inference over complex gamma-ray models.

GPU-accelerated probabilistic modeling for astrophysical data.

Framework applicable beyond gamma-ray data analysis.

Abstract

We motivate the use of differentiable probabilistic programming techniques in order to account for the large model-space inherent to astrophysical $\gamma$-ray analyses. Targeting the longstanding Galactic Center $\gamma$-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 $\gamma$-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.