arXiv:2604.08648v2 Announce Type: replace-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 efficie
The increasing sophistication and accessibility of differentiable programming combined with growing computational power allows for new approaches to complex scientific problems previously intractable.
This development indicates a powerful application of AI to fundamental scientific discovery, potentially accelerating our understanding of the universe and pushing the boundaries of data analysis in high-dimensional spaces.
The ability to simultaneously account for a continuum of possible models in astrophysical analyses, using probabilistic and GPU-accelerated methods, significantly enhances precision and reduces the need for heuristic assumptions.
- · Astrophysicists
- · Data scientists
- · GPU manufacturers
- · AI research institutions
- · Traditional statistical modeling approaches
- · Research groups lacking access to advanced compute
More accurate and faster inference for complex astrophysical phenomena, potentially leading to new discoveries.
The methodology could be applied to other scientific fields with large model-spaces, accelerating research across disciplines.
Enhanced scientific understanding could unlock technological advancements or new computational paradigms, influencing broader AI development.
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Read at arXiv cs.LG