Advancements in the IceAct Energy Spectrum Analysis

TL;DR

This paper discusses the progress in analyzing the cosmic ray energy spectrum using IceAct telescopes, which detect Cherenkov light from atmospheric particle interactions at the South Pole, employing advanced neural network techniques.

Contribution

It introduces the application of graph neural networks for reconstructing air shower properties from IceAct data, advancing cosmic ray spectrum analysis.

Findings

Successful implementation of graph neural networks for shower reconstruction

Progress in measuring the cosmic ray energy spectrum with IceAct

Enhanced understanding of cosmic ray interactions at high energies

Abstract

The IceAct telescopes are Imaging Air Cherenkov telescopes installed as part of the IceCube Neutrino Observatory at the geographic South Pole. They consist of a 61 pixel camera and are small and robust to withstand the harsh environmental conditions. IceAct detects Cherenkov light produced by cosmic-ray particles with energies above approximately 10\,TeV interacting inside the atmosphere, which is complementary to the measurement of the air shower at the surface by IceTop and the high-energy muons in the deep ice. Two telescopes have been taking data since 2019 with a conservative estimated duty cycle of around 10\%. A graph neural network is used to reconstruct the basic air shower properties, like geometry and primary energy. This work focuses on the current progress in analyzing the energy spectrum of cosmic rays using IceAct data.