Cosmic Ray Composition and Energy Spectrum from 1-30 PeV Using the 40-String Configuration of IceTop and IceCube

TL;DR

This paper presents a neural network analysis of cosmic ray composition and energy spectrum from 1 to 30 PeV using IceCube and IceTop data, providing insights into cosmic ray origins and characteristics.

Contribution

It introduces a novel neural network approach to analyze combined IceCube and IceTop data for cosmic ray composition and spectrum in the PeV range.

Findings

Cosmic ray composition varies across the energy spectrum.

The energy spectrum shows features around the knee region.

Neural network method improves composition and spectrum measurements.

Abstract

The mass composition of high energy cosmic rays depends on their production, acceleration, and propagation. The study of cosmic ray composition can therefore reveal hints of the origin of these particles. At the South Pole, the IceCube Neutrino Observatory is capable of measuring two components of cosmic ray air showers in coincidence: the electromagnetic component at high altitude (2835 m) using the IceTop surface array, and the muonic component above ~1 TeV using the IceCube array. This unique detector arrangement provides an opportunity for precision measurements of the cosmic ray energy spectrum and composition in the region of the knee and beyond. We present the results of a neural network analysis technique to study the cosmic ray composition and the energy spectrum from 1 PeV to 30 PeV using data recorded using the 40-string/40-station configuration of the IceCube Neutrino Observatory.