Unfolding the Atmospheric Muon Flux with IceCube: Investigating Stopping Muons and High-Energy Prompt Contributions

TL;DR

This paper uses IceCube data and machine learning to measure the atmospheric muon flux across a wide energy range, distinguishing between conventional and prompt muons to improve understanding of cosmic-ray interactions.

Contribution

It introduces a novel unfolding method for the muon energy spectrum in IceCube, including the first detailed analysis of the prompt muon component at PeV energies.

Findings

Unfolded the energy spectrum of stopping muons from a few hundred GeV to 10 TeV.

Provided the first measurements of the prompt muon flux at PeV energies.

Enhanced event reconstruction accuracy using machine learning techniques.

Abstract

Atmospheric muons produced in cosmic-ray air showers are classified as conventional muons from pion and kaon decays and prompt muons from heavy hadron decays. Conventional muons dominate at lower energies, and the prompt component becomes dominant at PeV energies and above. Precisely measuring the atmospheric muon flux from a few GeV to several PeV is valuable for advancing our understanding of cosmic-ray interactions and testing hadronic interaction models. Low-energy muons that stop within the IceCube in-ice array provide valuable information about the energy spectrum of muons from a few hundred GeV up to 10 TeV. Machine learning techniques are employed to enhance event reconstruction and selection to provide insights into the conventional and prompt components. This contribution presents the unfolding of the energy spectrum of stopping muons in IceCube as well as the unfolding of high-energy muons to probe the prompt component.