A Bayesian Approach to Neutrinoless Double Beta Decay Analysis in KamLAND-Zen

Abstract

Abstract Neutrinoless double beta decay (0vββ) is one of the major research interests in neutrino physics. The discovery of 0vββ would answer persistent puzzles in the standard model. KamLAND-Zen experiment is one of the leading efforts in the search for 0vββ. Since January 2019, data was collected using 745 kg of 136Xe and a frequentist likelihood analysis was used to set a limit on 0vββ half-life. 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 machine learning event classification algorithm to increase sensitivity.