Adaptive kernel density estimation for improved sky map computation in gamma-ray astronomy

• DOI: 10.1016/j.astropartphys.2024.102934
• Publication Date: 2024-02-03T16:26:42Z
• Authors: M. Holler; T. Mitterdorfer; S. Panny
• Keywords: Gamma-Ray Astronomy; Kernel density estimation; Kernel (algebra)

We introduce an alternative method for the calculation of sky maps from data taken with gamma-ray telescopes. In contrast to established smoothing 2D histogram reconstructed event directions a static kernel, we apply Kernel Density Estimation (KDE) where kernel size each candidate is related its estimated direction uncertainty. Exploiting this additional information implies gain in resulting image quality, which validated using both simulations and data. For tested simulation analysis configuration, achieved improvement can only be matched classical approach by removing events lower reconstruction reducing set considerable amount.