Hardware
High-dimensional inference for the gamma-ray sky with differentiable programming
arXiv:2604.08648v1 Announce Type: cross Abstract: We motivate the use of differentiable probabilistic programming techniques in order to account for the large model-space inherent to astrophysical gam
arXiv:2604.08648v1 Announce Type: cross 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.
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Source: arXiv cs.LG | 2026-04-13