Combined sensitivity of JUNO and KM3NeT/ORCA to the neutrino mass ordering

This article presents the potential of a combined analysis of the JUNO and KM3NeT/ORCA experiments to determine the neutrino mass ordering. This combination is particularly interesting as it significantly boosts the potential of either detector, beyond simply adding their neutrino mass ordering sens...

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Bibliographic Details
Published in:Journal of High Energy Physics
Main Authors: Aiello, S., Basegmez du Pree, S., Bouwhuis, M., Bruijn, R., Domi, A., van Eeden, T., van Eijk, D., Gatius, C., Heijboer, A., de Jong, M., de Jong, P., Jung, B.J., Kooijman, P., Melis, K.W., Muller, R., Ó Fearraigh, B., Pestel, V., Samtleben, D.F.E., Seneca, J., de Wolf, E.
Format: Article in Journal/Newspaper
Language:English
Published: 2022
Subjects:
Online Access:https://dare.uva.nl/personal/pure/en/publications/combined-sensitivity-of-juno-and-km3netorca-to-the-neutrino-mass-ordering(3e743536-636d-4547-8caf-2de3bc71c226).html
https://doi.org/10.1007/JHEP03(2022)055
https://hdl.handle.net/11245.1/3e743536-636d-4547-8caf-2de3bc71c226
https://pure.uva.nl/ws/files/125213967/JHEP03_2022_055.pdf
Description
Summary:This article presents the potential of a combined analysis of the JUNO and KM3NeT/ORCA experiments to determine the neutrino mass ordering. This combination is particularly interesting as it significantly boosts the potential of either detector, beyond simply adding their neutrino mass ordering sensitivities, by removing a degeneracy in the determination of ∆ m 2 31 between the two experiments when assuming the wrong ordering. The study is based on the latest projected performances for JUNO, and on simulation tools using a full Monte Carlo approach to the KM3NeT/ORCA response with a careful assessment of its energy systematics. From this analysis, a 5 σ determination of the neutrino mass ordering is expected after 6 years of joint data taking for any value of the oscillation parameters. This sensitivity would be achieved after only 2 years of joint data taking assuming the current global best-fit values for those parameters for normal ordering.