Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission

TL;DR

This paper develops deep learning models to predict the discrete gamma-ray emission from interstellar gas in the Milky Way, aiding in the analysis of Fermi-LAT data and the characterization of the Galactic center excess.

Contribution

It introduces convolutional neural networks to estimate unobserved gamma-ray emission components, improving modeling where gas tracer data is limited.

Findings

Deep learning effectively models gamma-ray emission in data-rich regions.

Method supports better discrimination of point sources from diffuse emission.

Potential to analyze unobserved regions of the galaxy.

Abstract

A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data, but modeling this emission relies on observations of rare gas tracers only available in limited regions of the sky. Identifying this contribution is important to discriminate gamma-ray point sources from interstellar gas, and to better characterize extended gamma-ray sources. We design and train convolutional neural networks to predict this emission where observations of these rare tracers do not exist and discuss the impact of this component on the analysis of the Fermi-LAT data. In particular, we evaluate prospects to exploit this methodology in the characterization of the Fermi-LAT Galactic center excess through accurate modeling of point-like structures in the data to help distinguish between a point-like or smooth nature for the excess. We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions, supporting prospects to employ these methods in yet unobserved regions.