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

2026-04-09

AI Summary

This work addresses the statistical inference challenge posed by the morphological uncertainty of the Galactic Center GeV Excess (GCE) in high-dimensional model spaces. For the first time, it introduces differentiable probabilistic programming into gamma-ray astrophysical analysis by constructing a differentiable probabilistic forward model and likelihood function. Leveraging GPU acceleration and vectorized computation, the proposed framework enables efficient and flexible full Bayesian inference of the GCE under continuous spatial morphology hypotheses. The method successfully performs joint inference across multiple plausible GCE spatial templates, demonstrating its feasibility, scalability, and significant advantages for large-scale astrophysical data analysis.

Technology Category

• Reasoning under Uncertainty: Probabilistic Programming
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• high-dimensional inference
• gamma-ray sky
• Galactic Center Excess
• model-space
• astrophysical analysis

Innovation

• differentiable programming
• probabilistic modeling
• gamma-ray astronomy
• variational inference
• GPU acceleration