Speaker
Description
The origin of Ultra-High-Energy Cosmic Rays (UHECRs) is one of the biggest mysteries in modern astrophysics.
Since UHECRs are deflected by Galactic and Extra-Galactic magnetic fields, their arrival directions do not point to their sources. Previous analyses conducted on the arrival directions of high energy events ($E>32\,\text{EeV}$) recorded by the Surface Detector of the Pierre Auger Observatory have not shown significant anisotropies. The largest excess found in the first $19$ years of data - at the $4.0\,\sigma$ level - is in the region around Centaurus A, and it is also the driving force of a correlation of UHECR arrival directions with a catalog of Starburst Galaxies, which is at the $3.8\,\sigma$ level.
Since UHECRs are mainly nuclei, the lightest ones (least charged) are also the least deflected. While the mass of the events can be estimated better using the Fluorescence Detector of the Pierre Auger Observatory, the Surface Detector provides the necessary statistics needed for astrophysical studies.
The introduction of novel mass-estimation techniques, such as machine learning models and an air-shower-universality based algorithm, will help in identifying high-rigidity events in the Surface Detector data of the Pierre Auger Observatory.
With this work, we present how event-per-event mass estimators can help enhance the sensitivity in the search for anisotropies in the arrival directions of UHECRs at small and intermediate angular scales using simulations.
