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Atmospheric muons from electromagnetic cascades

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 Publication date 2019
  fields Physics
and research's language is English




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Atmospheric muons are one of the main backgrounds for current Water- and Ice-Cherenkov neutrino telescopes designed to detect astrophysical neutrinos. The inclusive fluxes of atmospheric muons and neutrinos from hadronic interactions of cosmic rays have been extensively studied with Monte Carlo and cascade equation methods, for example, CORSIKA and MCEq. However, the muons that are pair produced in electromagnetic interaction of high energy photons are quantitatively not well understood. We present new simulation results and assess the model dependencies of the high-energy atmospheric muon flux including those from electromagnetic interactions, using a new numerical electromagnetic cascade equation solver EmCa that can be easily coupled with the hadronic solver MCEq. Both codes are in active development with the particular aim to become part of the next generation CORSIKA 8 air shower simulation package. The combination of EmCa and MCEq accounts for material effects that have not been previously included in most of the available codes. Hence, the influence of these effects on the air showers will also be briefly discussed.



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Electromagnetic-Cascades (EmCa) is a Python package for the simulation of electromagnetic cascades in various materials. The showers are modeled using cascade equations and the relevant interactions, specifically pair production, Bremsstrahlung, Compton scattering and ionization. This methodology has the advantage of being computationally inexpensive and fast, unlike Monte Carlo methods. The code includes low and high energy material effects, allowing for a high range of validity of the simulation results. EmCa is easily extendable and offers a framework for testing different electromagnetic interaction models. In combination with MCEq, a Python package for hadronic particle showers using cascade equations, full simulations of atmospheric fluxes can be done.
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Using the analytic modeling of the electromagnetic cascades compared with more precise numerical simulations we describe the physical properties of electromagnetic cascades developing in the universe on CMB and EBL background radiations. A cascade is initiated by very high energy photon or electron and the remnant photons at large distance have two-component energy spectrum, $propto E^{-2}$ ($propto E^{-1.9}$ in numerical simulations) produced at cascade multiplication stage, and $propto E^{-3/2}$ from Inverse Compton electron cooling at low energies. The most noticeable property of the cascade spectrum in analytic modeling is strong universality, which includes the standard energy spectrum and the energy density of the cascade $omega_{rm cas}$ as its only numerical parameter. Using numerical simulations of the cascade spectrum and comparing it with recent Fermi LAT spectrum we obtained the upper limit on $omega_{rm cas}$ stronger than in previous works. The new feature of the analysis is $E_{max}$ rule. We investigate the dependence of $omega_{rm cas}$ on the distribution of sources, distinguishing two cases of universality: the strong and weak ones.
We report on the first search for atmospheric and for diffuse astrophysical neutrino-induced showers (cascades) in the IceCube detector using 257 days of data collected in the year 2007-2008 with 22 strings active. A total of 14 events with energies above 16 TeV remained after event selections in the diffuse analysis, with an expected total background contribution of $8.3pm 3.6$. At 90% confidence we set an upper limit of $E^2Phi_{90%CL}<3.6times10^{-7} GeV cdot cm^{-2} cdot s^{-1}cdot sr^{-1} $ on the diffuse flux of neutrinos of all flavors in the energy range between 24 TeV and 6.6 PeV assuming that $Phi propto E^{-2}$ and that the flavor composition of the $ u_e : u_mu : u_tau$ flux is $1 : 1 : 1$ at the Earth. The atmospheric neutrino analysis was optimized for lower energies. A total of 12 events were observed with energies above 5 TeV. The observed number of events is consistent with the expected background, within the uncertainties.
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