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A Bayesian approach to the long-baseline neutrino oscillation sensitivity of DUNE
Preprint   Open access

A Bayesian approach to the long-baseline neutrino oscillation sensitivity of DUNE

S Abbaslu, F. Abd Alrahman, A. Abed Abud, R Acciarri, M. A Acero, M. R Adames, G Adamov, M Adamowski, K Adhikari, C Adriano, …
arXiv (Cornell University)
04 Aug 2026
DOI: https://doi.org/10.48550/arxiv.2608.04059
url
https://doi.org/10.48550/arxiv.2608.04059View
Preprint (Author's original) Open arXiv.org - Non-exclusive license to distribute

Abstract

Physics - High Energy Physics - Experiment Physics - High Energy Physics - Phenomenology
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$\sin^2 θ_{23}$and$\sin^2 θ_{13}$ . We exploit the flexibility of the Bayesian framework to incorporate parameter constraints post hoc and assess the impact of applying a reactor short-baseline$θ_{13}$constraint. A significant increase in the sensitivity to the$θ_{23}$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.

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