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T: Fachverband Teilchenphysik
T 41: Cosmic Ray II
T 41.3: Vortrag
Dienstag, 21. März 2023, 17:30–17:45, POT/0013
A machine learning approach to mass composition studies of ultra-high energy cosmic rays with the AugerPrime upgrade of the Pierre Auger Observatory. — •Akash Parmar, Paulo Ferreira, and Thomas Hebbeker — RWTH Aachen University, Aachen, Germany
The Pierre Auger Observatory is the world’s largest experiment to observe the extensive air showers produced by ultra-high energy cosmic rays. The observatory uses a hybrid detection method that combines 1600 ground-based water Cherenkov detectors covering an area of more than 3000 km2 and 27 fluorescence detectors at four sites. The efficiency and measurement techniques of the Pierre Auger observatory are improved by the ongoing upgrade called AugerPrime. A part of the upgrade consists of deploying a scintillator detector on top of each water Cherenkov detector which provides additional information about the composition of the extensive air showers.
Currently, the understanding of cosmic rays at ultra-high energy is limited by low incoming flux and the available theoretical models for hadronic interactions. Precise measurement of the composition can help us understand the sources of cosmic rays and improve the current models.
The additional information provided by the combination of water Cherenkov detectors and scintillator surface detectors has been explored with a machine learning algorithm called random forest, to analyze the measurable properties of the shower and infer the mass composition of the primary particle.