Abstract We study the analysis method to determine the cosmic-ray energy spectra of different mass groups assuming the use of the Tibet ASγ experiment, which consists of the high-density Tibet air-shower array (Tibet-AS) and the underground muon detector (MD) array. These arrays measure the sampling air shower size Σρ and the total muon number ΣNμ of each air shower event. These parameters are known to contain information on the energy and mass of the primary particle. To reconstruct the energy spectra of individual cosmic-ray mass groups, we apply a multidimensional unfolding method based on Bayes’ theorem to the two-dimensional distribution of Σρ and ΣNμ produced by Monte Carlo simulation. Simulated datasets with combinations of the EPOS-LHC, SIBYLL-2.3c, and QGSJET II-04 high-energy hadronic interaction models and a helium-dominant composition model are analyzed while using a response matrix produced by EPOS-LHC. The unfolded spectra of the EPOS + helium-dominant composition model dataset show a deviation from the input flux within ±10% except for a few bins, meaning that the uncertainty of the technique itself and the composition model dependence is at that level. It is also shown that the deviation in the all-particle spectrum is within ±10% even when using different hadronic interaction models in the dataset and the response matrix. On the other hand, the unfolded spectra of individual mass groups have a clear dependence on the hadronic interaction model. The model dependence of the proton and helium spectra amounts to ±25% below 106.5 GeV. The dependence in the carbon group is at a ±25% level below 106 GeV, and for the iron spectrum, it amounts to +55% and −30% in the energy range of 105.1 GeV to 106.7 GeV. We are now able to measure individual energy spectra in the knee region—including those of heavy nuclei, which remain poorly understood. Thanks to the recent extension of direct measurements beyond 104 GeV, a comparison between our analysis and the direct measurements will provide a good test of the hadronic interaction models.
Imaizumi et al. (Thu,) studied this question.
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