"The zen of neutrino: neutrinoless double beta decay and deep learning"
Neutrinoless Double Beta Decay(0νββ) is one of the major research interests in neutrino physics. The discovery of 0νββ would answer persistent puzzles in the standard model. KamLAND-ZEN experiment is one of the leading efforts in the search of 0νββ. The data is taken from 380kg of Xe136 isotopes, and analyzed by a Frequentisit likelihood analysis to set limit on 0νββ lifetime. In addition to the well-established Frequentist approach, we conduct a Bayesian analysis with a Markov Chain Monte Carlo(MCMC). The Bayesian approach allows us to use modern statistical tools and serves as a cross check of the Frequentist analysis; furthermore, we provide the possibility of adding a self-developed machine learning event classification algorithm to increase sensitivity. In this talk, we will present the analysis framework and result of this Bayesian approach, as well as the future possible inclusion of machine learning result.