Inferring the Morphology of the Galactic Center Excess with Gaussian Processes
Edward D. Ramirez, Yitian Sun, Matthew R. Buckley, Siddharth, Mishra-Sharma, and Tracy R. Slatyer

TL;DR
This paper introduces a non-parametric Gaussian process model to analyze the Galactic Center Excess in gamma-ray data, revealing complex morphological features and assessing the impact of modeling choices on physical interpretations.
Contribution
It is the first to apply a flexible Gaussian process approach to model the GCE, capturing complex morphologies and systematically evaluating template-based models.
Findings
The Gaussian process model recovers known templates in synthetic data.
The best-fit model includes a bulge and NFW squared component.
Novel morphological features like localized sources were identified.
Abstract
Descriptions of the Galactic Center using Fermi gamma-ray data have so far modeled the Galactic Center Excess (GCE) as a template with fixed spatial morphology or as a linear combination of such templates. Although these templates are informed by various physical expectations, the morphology of the excess is a priori unknown. For the first time, we describe the GCE using a flexible, non-parametric machine learning model -- the Gaussian process (GP). We assess our model's performance on synthetic data, demonstrating that the model can recover the templates used to generate the data. We then fit the \Fermi data with our model in a single energy bin from 2-20 GeV (leaving a spectral GP analysis of the GCE for future work) using a variety of template models of diffuse gamma-ray emission to quantify our fits' systematic uncertainties associated with diffuse emission modeling. We interpret…
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Taxonomy
TopicsAstronomy and Astrophysical Research
MethodsGaussian Process
