2026/08/04 by DUNE Collaboration, S. Abbaslu, F. Abd Alrahman +1278
Physics and Astronomy · #hep-ex #hep-ph
20 pages, 5 figures
arxiv created 2026/08/04 · arxiv updated 2026/08/06
The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is evaluated using a Bayesian Markov Chain Monte Carlo (MCMC) approach. This analysis uses the same underlying sensitivity inputs as previous DUNE studies [Eur. Phys. J. C 80, 978 (2020)], and therefore does not present updated DUNE sensitivities, but instead explores the additional inferences accessible using a Bayesian approach. We present four-dimensional posterior probability distributions of the oscillation parameters, highlighting the breadth of correlation in the parameter space of interest, especially between sin2 θ23 and sin2 θ13. We exploit the flexibility of the Bayesian framework to incorporate parameter constraints post hoc and assess the impact of applying a reactor short-baseline θ13 constraint. A significant increase in the sensitivity to the θ23 octant is found when including the constraint. Posterior distributions of derived quantities can be easily constructed from MCMC results. This work presents the first study of DUNE's sensitivity to the Jarlskog invariant, J, a quantity that provides a parametrisation-independent measure of charge-parity violation in the leptonic sector.