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Neural Networks as a Composition Diagnostic for Ultra-high Energy Cosmic Rays

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 Publication date 2005
  fields Physics
and research's language is English




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We analyze here the possibility of studying mass composition in the Auger data sample using neural networks as a diagnostic tool. Extensive air showers were simulated using the AIRES code, for the two hadronic interaction models in current use: QGSJet and Sibyll. Both, photon and hadron primaries were simulated and used to generate events. The output parameters from the ground array were simulated for the typical instrumental and environmental conditions at the Malargue Auger site using the code SAMPLE. Besides photons, hydrogen, helium, carbon, oxygen, magnesium, silicon, calcium and iron nuclei were also simulated. We show that Principal Components Analysis alone is enough to separate individual photon from hadron events, but the same technique cannot be applied to the classification of hadronic events. The latter requires the use of a more robust diagnostic. We show that neural networks are potentially powerful enough to discriminate proton from iron events almost on an event-by-event basis. However, in the case of a more realistic multi-component mixture of primary nuclei, only a statistical estimate of the average mass can be reliably obtained. Although hybrid events are not explicitly simulated, we show that, whenever hybrid information in the form of $X_{max}$ is introduced in the training procedure of the neural networks, a considerable improvement can be achieved in mass discrimination analysis.



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Using the Auger mass-composition analysis of ultra high energy cosmic rays, based on the shape-fitting of $X_{max}$ distributions, we demonstrate that mass composition and energy spectra measured by Auger, Telescope Array and HiRes can be brought into good agreement. The shape-fitting analysis of $X_{max}$ distributions shows that the measured sum of proton and Helium fractions, for some hadronic-interaction models, can saturate the total flux. Such p+He model, with small admixture of other light nuclei, naturally follows from cosmology with recombination and reheating phases. The most radical assumption of the presented model is the assumed unreliability of the experimental separation of Helium and protons, which allows to consider He/p ratio as a free parameter. The results presented here show that the models with dominant p+He composition explain well the energy spectrum of the dip in the latest (2015 - 2017) data of Auger and Telescope Array, but have some tension at the highest energies with the expected Greisen-Zatsepin-Kuzmin cutoff. The Auger-Prime upgrade experiment has a great potential to reject or confirm this model.
We use a kinetic-equation approach to describe the propagation of ultra high energy cosmic ray protons and nuclei and calculate the expected spectra and mass composition at the Earth for different assumptions on the source injection spectra and chemical abundances. When compared with the spectrum, the elongation rate $X_{max}(E)$ and dispersion $sigma(X_{max})$ as observed with the Pierre Auger Observatory, several important consequences can be drawn: a) the injection spectra of nuclei must be very hard, $sim E^{-gamma}$ with $gammasim 1-1.6$; b) the maximum energy of nuclei of charge $Z$ in the sources must be $sim 5Ztimes 10^{18}$ eV, thereby not requiring acceleration to extremely high energies; c) the fit to the Auger spectrum can be obtained only at the price of adding an {it ad hoc} light extragalactic component with a steep injection spectrum ($sim E^{-2.7}$). In this sense, at the ankle ($E_{A}approx 5times 10^{18}$ eV) all the components are of extragalactic origin, thereby suggesting that the transition from Galactic to extragalactic cosmic rays occurs below the ankle. Interestingly, the additional light extragalactic component postulated above compares well, in terms of spectrum and normalization, with the one recently measured by KASCADE-Grande.
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