Abstract
High-energy atmospheric muons can yield information about the prompt contribution to atmospheric lepton fluxes. Relevant to studying the flux of astrophysical neutrinos, this also complements results from collider experiments in the forward region. A machine-learning based selection has been developed, identifying high-energy ($E_{\mu} \gtrsim$ 1 TeV) leading muons which dominate the energy losses detected in IceCube. The sample is then analyzed in two ways. First, the correlation between the muon energy in ice and the muon energy at its production in the atmosphere, which can be derived from simulations based on Monte-Carlo methods, is used for estimating the differential energy spectrum of atmospheric muons in the energy range between 6 and 400 TeV. The best-fit power law index describing the atmospheric muon flux is found to be consistent with the result of a previous analysis. Second, dedicated simulations are used to show a proof-of-concept for reconstructing the effective Feynman-$x$ of atmospheric muons by combining information from IceCube and IceTop. A robust correlation between true and reconstructed effective Feynman-$x$ is found, enabling future studies of this quantity with the IceCube Neutrino Observatory.
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