Cosmic rays primary energy estimation using Machine Learning and combined reconstruction

The IceCube Neutrino Observatory at the South Pole is capable of measuring two components of the cosmic rays air shower. The electromagnetic component using a km2 surface array IceTop, and the high-energy muonic component using km3 in-ice array IceCube between 1.5 and 2.5 km below the surface. The combination of both arrays in conjunction with a new flexible curvature and new timing fluctuation function provides an opportunity for possible improvements of cosmic rays reconstruction. This work presents a preliminary investigation of possible improvements of cosmic rays primary energy estimation (proton, iron, helium, and oxygen) by using Machine Learning techniques and combined reconstruction.