High-dimensional inference for the $γ$-ray sky with differentiable programming
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 probabil...
| Authors | Siddharth Mishra-Sharma, Tracy R. Slatyer, Yitian Sun et al. |
|---|---|
| Primary category | astro-ph.HE |
| Published | 9 April 2026 |
| Last updated at source | 26 June 2026 |
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