In the context of astrophysical searches for dark matter (DM), a traditional limitation concerns the physical modelling of background contributions (i.e. everything which is not DM). In this work we build a background data-driven model by generalising a kernel density estimation procedure as originally proposed by D.F. Specht in 1991. The free parameters of the model are obtained via direct optimisation of the resultant Likelihood. Data is taken from the public database of the Fermi-LAT collaboration (NASA), and consists of energy-binned gamma-ray counts at any given position in the sky. Finally, we use our model to predict the background contribution at dwarf spheroidal satellite galaxies, which are arguably the cleanest laboratories to search for non-gravitational interactions of dark matter.
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