# Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory

## TL;DR

This paper demonstrates how machine learning techniques applied to IceCube's multi-component detector data can improve the measurement of cosmic ray composition around 3 PeV, shedding light on the galactic to extragalactic transition.

## Contribution

It introduces a novel application of machine learning to combined IceCube detector data for improved cosmic ray composition analysis at PeV energies.

## Key findings

- Enhanced reconstruction performance for cosmic-ray energy and mass.
- Improved sensitivity to cosmic-ray composition in the knee region.
- Potential insights into the galactic to extragalactic transition.

## Abstract

The evaluation of mass composition of cosmic rays in the knee region ($\sim 3$ PeV) is critical to understanding the transition in the origin of cosmic rays from galactic to extragalactic sources. The IceCube Neutrino Observatory at the South Pole is a multi-component detector consisting of the surface IceTop array and the deep in-ice IceCube detector. By applying modern machine-learning techniques to cosmic-ray air showers reconstructed coincidentally in both detector components of IceCube observatory, the energy and the mass of primary cosmic rays in this transition region can be measured. In this contribution, we will discuss the reconstruction performance and composition sensitivity of IceCube observables presently under development.