High-energy muons from air shower events detected in IceCube are selected using state of the art machine learning algorithms. Attributes to distinguish a HE-muon event from the background of low-energy muon bundles are selected using the mRMR algorithm and the events are classified by a random forest model. In a subsequent analysis step the obtained sample is used to reconstruct the atmospheric muon energy spectrum, using the unfolding software TRUEE. The reconstructed spectrum covers an energy range from 104GeV to 106GeV. The general analysis scheme is presented, including results using the first year of data taken with IceCube in its complete configuration with 86 instrumented strings.
@article{arxiv.1701.04067,
title = {Development of a Machine Learning Based Analysis Chain for the Measurement of Atmospheric Muon Spectra with IceCube},
author = {Tomasz Fuchs},
journal= {arXiv preprint arXiv:1701.04067},
year = {2017}
}