Galactic Center Excess in a New Light: Disentangling the γ-Ray Sky with Bayesian Graph Convolutional Neural Networks

Observational Cosmology with Artificial Neural Networks

In cosmology, the analysis of observational evidence is very important when testing theoretical models of the Universe. Artificial neural networks are powerful and versatile computational tools for data modelling and have recently been considered in the analysis of cosmological data. The main goal of this paper is to provide an introduction to artificial neural networks and to describe some of their applications to cosmology. We present an overview on the fundamentals of neural networks and their technical details. Through three examples, we show their capabilities in the modelling of cosmological data, numerical tasks (saving computational time), and the classification of stellar objects. Artificial neural networks offer interesting qualities that make them viable alternatives for data analysis in cosmological research.

Dissecting the Inner Galaxy with γ-Ray Pixel Count Statistics

We combine adaptive template fitting and pixel count statistics in order to assess the nature of the Galactic Center excess in Fermi-LAT data. We reconstruct the flux distribution of point sources well below the Fermi-LAT detection threshold, and measure their radial and longitudinal profiles in the inner Galaxy. We find that all point sources and the bulge-correlated diffuse emission each contributes O(10%) of the total inner Galaxy emission, and disclose a potential subthreshold point-source contribution to the Galactic Center excess.