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Hybrid method for identifying mass groups of primary cosmic rays in the joint operation of IACTs and wide angle Cherenkov timing arrays
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This work is a methodical study of another option of the hybrid method originally aimed at gamma/hadron separation in the TAIGA experiment. In the present paper this technique was performed to distinguish between different mass groups of cosmic rays in the energy range 200 TeV - 500 TeV. The study was based on simulation data of TAIGA prototype and included analysis of geometrical form of images produced by different nuclei in the IACT simulation as well as shower core parameters reconstructed using timing array simulation. We show that the hybrid method can be sufficiently effective to precisely distinguish between mass groups of cosmic rays.
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Machine Learning in Gamma Astronomy
This paper reviews deep learning approaches for Imaging Atmospheric Cherenkov Telescope data, covering classification, parameter reconstruction, and generative modeling, with a focus on references.
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