Search for Diffuse Supernova Neutrino Background with 956.2 days of Super-Kamiokande Gadolinium Dataset

TL;DR

This paper reports a search for the diffuse supernova neutrino background using over 956 days of gadolinium-enhanced Super-Kamiokande data, employing new machine learning techniques and improved background reduction, resulting in upper limits on neutrino flux.

Contribution

The study introduces new machine learning neutron detection algorithms and enhanced background reduction methods in a large-scale neutrino search, extending previous analyses.

Findings

No significant excess detected over background.

Set 90% confidence level upper limits on neutrino flux.

Observed a 1.2 sigma deviation consistent with previous results.

Abstract

We report the search result for the Diffuse Supernova Neutrino Background (DSNB) in neutrino energies beyond 9.3~MeV in the gadolinium-loaded Super-Kamiokande (SK) detector with $22,500\times956.2$$~\rm m^3\cdot day$ exposure. %$22.5{\rm k}\times956.2$$~\rm m^3\cdot day$ exposure. Starting in the summer of 2020, SK introduced 0.01\% gadolinium (Gd) by mass into its ultra-pure water to enhance the neutron capture signal, termed the SK-VI phase. This was followed by a 0.03\% Gd-loading in 2022, a phase referred to as SK-VII. We then conducted a DSNB search using 552.2~days of SK-VI data and 404.0~days of SK-VII data through September 2023. This analysis includes several new features, such as two new machine-learning neutron detection algorithms with Gd, an improved atmospheric background reduction technique, and two parallel statistical approaches. No significant excess over background predictions was found in a DSNB spectrum-independent analysis, and 90\% C.L. upper limits on the astrophysical electron anti-neutrino flux were set. Additionally, a spectral fitting result exhibited a $\sim1.2\sigma$ disagreement with a null DSNB hypothesis, comparable to a previous result from 5823~days of all SK pure water phases.