Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission
This work addresses the challenge of distinguishing gamma-ray point sources from interstellar gas for astrophysicists analyzing Fermi-LAT data, though it is incremental as it applies existing deep learning methods to a new domain-specific problem.
The researchers tackled the problem of modeling the discrete component of galactic interstellar gamma-ray emission, which is limited by scarce tracer observations, by designing convolutional neural networks to predict this emission in unobserved regions, showing that deep learning can effectively model it with statistical significance in data-rich areas.
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.