Flavor and energy inference for the high-energy IceCube neutrinos

We present a flavor and energy inference analysis for each down-going high-energy astrophysical neutrino event observed by the IceCube observatory during six years of data taking. Our goal is to obtain, for the first time, an estimate of the posterior probability distribution for the most relevant properties, such as the neutrino energy and flavor, of the neutrino-nucleon interactions producing shower and track events in the IceCube detector. For each event the main observables in the IceCube detector are the deposited energy and the event topology (showers or tracks) produced by the Cherenkov light by the transit through a medium of charged particles created in neutrino interactions. It is crucial to reconstruct from these observables the properties of the neutrino which generated such event. Here we describe how to achieve this goal using Bayesian inference and Markov chain Monte Carlo methods.