2020/02/29 by Bhaskar Dutta, Rafael F. Lang, Shu Liao +3
Physics and Astronomy · #Borexino #Dark Matter and Cosmic Phenomena #Dark matter #MAJORANA #Neutrino #Neutrino Physics Research #Neutrino oscillation #Parameter space #Particle physics theoretical and experimental studies #Scattering #Solar neutrino #hep-ph
paper · pdf · doi:10.1007/jhep09(2020)106
published as J. High Energ. Phys. 2020, 106 (2020) · Published in JHEP. References and plots updated, CENNS-10 analysis updated. Codes can be provided upon request
openalex created_date 2020/02/14 · openalex publication_date 2020/09/01 · arxiv created 2020/09/20 · arxiv updated 2020/09/22 · openalex updated_date 2026/08/06
A bstract Neutrino non-standard interactions (NSI) with the first generation of standard model fermions can span a parameter space of large dimension and exhibit degeneracies that cannot be broken by a single class of experiment. Oscillation experiments, together with neutrino scattering experiments, can merge their observations into a highly informational dataset to combat this problem. We consider combining neutrino-electron and neutrino-nucleus scattering data from the Borexino and COHERENT experiments, including a projection for the upcoming coherent neutrino scattering measurement at the CENNS-10 liquid argon detector. We extend the reach of these data sets over the NSI parameter space with projections for neutrino scattering at a future multi-ton scale dark matter detector and future oscillation measurements from atmospheric neutrinos at the Deep Underground Neutrino Experiment (DUNE). In order to perform this global anal- ysis, we adopt a novel approach using the copula method, utilized to combine posterior information from different experiments with a large, generalized set of NSI parameters. We find that the contributions from DUNE and a dark matter detector to the Borexino and COHERENT fits can improve constraints on the electron and quark NSI parameters by up to a factor of 2 to 3, even when relatively many NSI parameters are left free to vary in the analysis.