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

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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.