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

TL;DR

This work reports a search for the Diffuse Supernova Neutrino Background (DSNB) using $956.2$ days of gadolinium-loaded Super-Kamiokande data (SK-Gd) from the SK-VI and SK-VII phases with an exposure of $22{,}500\times956.2~\mathrm{m^3\,day}$. It introduces two new neutron-detection approaches (including ML-based methods) and advanced background-reduction techniques, along with two parallel statistical analyses to extract a potential DSNB signal. No significant excess over background predictions is observed, yielding a 90% C.L. upper limit on the astrophysical $ar{ u}_e$ flux, and a spectral-fit result showing a $1.2\sigma$ disagreement with the null DSNB hypothesis, comparable to prior SK results using pure-water data. The study demonstrates SK-Gd’s capability to constrain the DSNB and informs future modeling of the cosmic CCSN history and neutrino emission scenarios.

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 $\sim1.2σ$ disagreement with a null DSNB hypothesis, comparable to a previous result from 5823~days of all SK pure water phases.

Figures (5)

• Figure 1: DSNB $\bar{\nu}_e$ flux predictions from various predictions Nakazato2024Martinez2024Nick2024Ashida2023Ivanez2023Horiuchi2021Kresse2021Tabrizi2021Barranco2018Horiuchi2018Nakazato2015Galais2010Horiuchi2009Kaplinghat2000Malaney1997Hartmann1997. Representative parameter sets are chosen for some of the flux models: "NH" and "IH" represent the neutrino normal and inverted mass hierarchy assumptions in the calculation, respectively. "HB06" and "MD14" refer to the calculation of Hopkins_2006 and Madau_2014 for the SFR, respectively. "SH" represents the "strongly hierarchical" defined in Ivanez2023. For the Nakazato2024 model, only the fallback supernova contribution to the DSNB is shown. We only draw the maximum flux model of Horiuchi2009 model with a neutrino temperature of 6 MeV. Refer to each publication for further detailed descriptions.
• Figure 2: Illustration of the separation for pre- and post-sample regions.
• Figure 3: $\Delta l$ distribution between a DSNB candidate and the muon events before and after the candidate.
• Figure 4: NCQE event reduction efficiency for each cut step in SK-VI. These lines show accumulated efficiencies at each stage.
• Figure 11: Illustration of neutron cloud variables.