Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory. (arXiv:1908.06433v1 [astro-ph.HE])
<a href="http://arxiv.org/find/astro-ph/1/au:+Plum_M/0/1/0/all/0/1">Matthias Plum</a> (for the IceCube Collaboration)
The evaluation of mass composition of cosmic rays in the knee region ($sim
3$ PeV) is critical to understanding the transition in the origin of cosmic
rays from galactic to extragalactic sources. The IceCube Neutrino Observatory
at the South Pole is a multi-component detector consisting of the surface
IceTop array and the deep in-ice IceCube detector. By applying modern
machine-learning techniques to cosmic-ray air showers reconstructed
coincidentally in both detector components of IceCube observatory, the energy
and the mass of primary cosmic rays in this transition region can be measured.
In this contribution, we will discuss the reconstruction performance and
composition sensitivity of IceCube observables presently under development.
The evaluation of mass composition of cosmic rays in the knee region ($sim
3$ PeV) is critical to understanding the transition in the origin of cosmic
rays from galactic to extragalactic sources. The IceCube Neutrino Observatory
at the South Pole is a multi-component detector consisting of the surface
IceTop array and the deep in-ice IceCube detector. By applying modern
machine-learning techniques to cosmic-ray air showers reconstructed
coincidentally in both detector components of IceCube observatory, the energy
and the mass of primary cosmic rays in this transition region can be measured.
In this contribution, we will discuss the reconstruction performance and
composition sensitivity of IceCube observables presently under development.
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