Measurement of solar neutrino interaction rate below 3.49 MeV in Super-Kamiokande-IV

TL;DR

This study measures solar neutrino interaction rates below 3.49 MeV in Super-Kamiokande-IV, utilizing machine learning for background reduction, and observes a significant neutrino signal in the 2.99-3.49 MeV range, providing insights into neutrino flavor conversion.

Contribution

It introduces a novel analysis of low-energy solar neutrinos using machine learning techniques to reduce background, enabling measurement below previous energy thresholds.

Findings

Detected solar neutrino signal between 2.99 and 3.49 MeV with 2.76σ significance.

Observed a data to unoscillated MC ratio of approximately 0.307.

Low-energy data slightly influence the best-fit parameters in the spectral analysis.

Abstract

Super-Kamiokande has observed $^{8}\text{B}$ solar neutrino elastic scattering at recoil electron kinetic energies ($E_{kin}$) as low as 3.49 MeV to study neutrino flavor conversion within the sun. At SK-observable energies, these conversions are dominated by the Mikheyev-Smirnov-Wolfenstein effect. An upturn in the electron neutrino survival probability in which vacuum neutrino oscillations become dominant is predicted to occur at lower energies, but radioactive background increases exponentially with decreasing energy. New machine learning approaches provide substantial background reduction below 3.49 MeV such that statistical extraction of solar neutrino interactions becomes feasible. This article presents an analysis of the solar neutrino interaction rate at $E_{kin}$ < 3.49 MeV with the full SK-IV period, using data from a wideband intelligent trigger when available and with a boosted decision tree for event selection. A solar neutrino signal is observed between 2.99 MeV < $E_{kin}$ < 3.49 MeV with $2.76\sigma$ significance and a data to unoscillated MC ratio of $0.307^{+0.112}_{-0.111}$. This additional low energy data has a negligible effect on the $1\sigma$ intervals of the fits to the solar neutrino energy spectrum but has a noticeable effect on the best fit when using the exponential parameterization.