High-dimensional inference for the $γ$-ray sky with differentiable programming
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917398146187264 |
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| author | Mishra-Sharma, Siddharth Slatyer, Tracy R. Sun, Yitian Wu, Yuqing |
| author_facet | Mishra-Sharma, Siddharth Slatyer, Tracy R. Sun, Yitian Wu, Yuqing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08648 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | High-dimensional inference for the $γ$-ray sky with differentiable programming Mishra-Sharma, Siddharth Slatyer, Tracy R. Sun, Yitian Wu, Yuqing High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Machine Learning High Energy Physics - Phenomenology 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. |
| title | High-dimensional inference for the $γ$-ray sky with differentiable programming |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Machine Learning High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2604.08648 |