Model Independent Approach of the JUNO $^8$B Solar Neutrino Program
JUNO Collaboration: Jie Zhao, Baobiao Yue, Haoqi Lu, Yufeng Li, Jiajie, Ling, Zeyuan Yu, Angel Abusleme, Thomas Adam, Shakeel Ahmad, Rizwan Ahmed,, Sebastiano Aiello, Muhammad Akram, Abid Aleem, Tsagkarakis Alexandros,, Fengpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov

TL;DR
This paper proposes a model-independent method for detecting $^8$B solar neutrinos at JUNO using multiple interaction channels, achieving high precision in flux and oscillation parameters with potential to advance solar and neutrino physics.
Contribution
It introduces a novel, model-independent approach utilizing CC, NC, and ES channels at JUNO, leveraging large $^{13}$C mass and low background for improved neutrino detection.
Findings
Achieves 5% precision in $^8$B neutrino flux measurement with 10 years of data.
Demonstrates potential for 8% and 20% precision in $ heta_{12}$ and $ riangle m^2_{21}$.
When combined with SNO, can reach 3% precision in $^8$B flux measurement.
Abstract
The physics potential of detecting B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model independent manner by using three distinct channels of the charged-current (CC), neutral-current (NC) and elastic scattering (ES) interactions. Due to the largest-ever mass of C nuclei in the liquid-scintillator detectors and the {expected} low background level, B solar neutrinos would be observable in the CC and NC interactions on C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC and ES channels to guarantee the B solar neutrino observation. From the sensitivity studies performed in this work, we show…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
