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The evaluation of mass composition of cosmic rays in the knee region ($\sim3$ PeV) is critical to understanding the transition in the origin of cosmicrays from galactic to extragalactic sources. The IceCube Neutrino Observatoryat the South Pole is a multi-component detector consisting of the surfaceIceTop array and the deep in-ice IceCube detector. By applying modernmachine-learning techniques to cosmic-ray air showers reconstructedcoincidentally in both detector components of IceCube observatory, the energyand the mass of primary cosmic rays in this transition region can be measured.In this contribution, we will discuss the reconstruction performance andcomposition sensitivity of IceCube observables presently under development.

Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory