Search for Diffuse Supernova Neutrino Background in the Full KamLAND Dataset with Neural-network-based Event Classification

We report a search for the diffuse supernova neutrino background (DSNB) with the KamLAND detector, targeting electron antineutrinos ( ν ¯ e ) via inverse beta decay in the neutrino energy range of 8.3–30.8 MeV. Using an exposure of 9.02 kton-yr (8.3–9.3 MeV) and 9.42 kton-yr (9.3–30.8 MeV) with a liquid scintillator, we observe seven candidate events after applying a new deep-neural-network-based event classification technique. This is consistent with the background-only expectation of 16.2 ± 9.4 events, which includes systematic uncertainties associated with the neural-network selection. A spectral analysis of the energy and radial distributions finds no significant excess attributable to the DSNB. We therefore set 90% confidence-level upper limits on the DSNB flux of 38–43 cm −2 s −1 , depending on the assumed DSNB model. We also derive model-independent 90% confidence-level upper limits on the ν ¯ e flux, which are among the most stringent constraints below 13.3 MeV. Beyond the DSNB search itself, this work establishes neural-network-based event classification as a promising approach for suppressing neutron-associated backgrounds in liquid-scintillator neutrino detectors. The improved sensitivity also enables an astrophysical interpretation of the average neutrino emission from core-collapse supernovae and highlights the complementary role of low-threshold liquid-scintillator detectors in future DSNB searches.