2017/01/15 by Tomasz Fuchs, Fuchs, Tomasz
Physics and Astronomy · #Astrophysics and Cosmic Phenomena #FOS: Physical sciences #High Energy Astrophysical Phenomena (astro-ph.HE) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #astro-ph.HE #astro-ph.IM
paper · pdf · doi:10.48550/arxiv.1701.04067
XXV ECRS 2016 Proceedings - eConf C16-09-04.3
arxiv created 2017/01/15 · openalex publication_date 2017/01/15 · arxiv updated 2017/01/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 104 GeV to 106 GeV. 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.