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

@misc{chernyak2026search,
  title = {{Search for Diffuse Supernova Neutrino Background in the Full KamLAND Dataset with Neural-Network-Based Event Classification}},
  author = {D. Chernyak and T. Eda and M. Eizuka and R. Endo and A. Gando and Y. Gando and T. Hachiya and F. Haneishi and K. Hata and T. Hirai and K. Hosokawa and K. Ichimura and H. Ikeda and K. Inoue and K. Ishidoshiro and Y. Kamei and N. Kawada and Y. Kishimoto and M. Koga and K. Mikami and H. Miyake and K. Mizukoshi and D. Morita and K. Nakamura and R. Nakamura and J. Nakane and Y. Nakano and N. Obata and Y. Ota and K. Saito and I. Sakaki and I. Shimizu and J. Shirai and A. Suzuki and K. Tachibana and A. Takeuchi and K. Tamae and H. Watanabe and Z. Xu and S. Yoshida and S. Umehara and K. Fushimi and K. Kotera and Y. Urano and S. Kurosawa and H. Ozaki and B. E. Berger and B. K. Fujikawa and J. G. Learned and J. Maricic and Z. Li and L. A. Winslow and Y. Efremenko and H. J. Karwowski and D. M. Markoff and W. Tornow and S. Dell'Oro and T. O'Donnell and J. A. Detwiler and S. Enomoto},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2606.29381},
  eprint = {2606.29381},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.29381},
}

TY  - UNPB
TI  - Search for Diffuse Supernova Neutrino Background in the Full KamLAND Dataset with Neural-Network-Based Event Classification
AU  - D. Chernyak
AU  - T. Eda
AU  - M. Eizuka
AU  - R. Endo
AU  - A. Gando
AU  - Y. Gando
AU  - T. Hachiya
AU  - F. Haneishi
AU  - K. Hata
AU  - T. Hirai
AU  - K. Hosokawa
AU  - K. Ichimura
AU  - H. Ikeda
AU  - K. Inoue
AU  - K. Ishidoshiro
AU  - Y. Kamei
AU  - N. Kawada
AU  - Y. Kishimoto
AU  - M. Koga
AU  - K. Mikami
AU  - H. Miyake
AU  - K. Mizukoshi
AU  - D. Morita
AU  - K. Nakamura
AU  - R. Nakamura
AU  - J. Nakane
AU  - Y. Nakano
AU  - N. Obata
AU  - Y. Ota
AU  - K. Saito
AU  - I. Sakaki
AU  - I. Shimizu
AU  - J. Shirai
AU  - A. Suzuki
AU  - K. Tachibana
AU  - A. Takeuchi
AU  - K. Tamae
AU  - H. Watanabe
AU  - Z. Xu
AU  - S. Yoshida
AU  - S. Umehara
AU  - K. Fushimi
AU  - K. Kotera
AU  - Y. Urano
AU  - S. Kurosawa
AU  - H. Ozaki
AU  - B. E. Berger
AU  - B. K. Fujikawa
AU  - J. G. Learned
AU  - J. Maricic
AU  - Z. Li
AU  - L. A. Winslow
AU  - Y. Efremenko
AU  - H. J. Karwowski
AU  - D. M. Markoff
AU  - W. Tornow
AU  - S. Dell'Oro
AU  - T. O'Donnell
AU  - J. A. Detwiler
AU  - S. Enomoto
PY  - 2026
DA  - 2026/10/20
PB  - arXiv
DO  - 10.48550/arxiv.2606.29381
UR  - https://arxiv.org/abs/2606.29381
AB  - We report a search for the diffuse supernova neutrino background (DSNB) with the KamLAND detector, targeting electron antineutrinos via inverse beta decay in the neutrino energy range of 8.3 to 30.8 MeV. Using liquid-scintillator exposures of 9.02 kton-year for 8.3 to 9.3 MeV and 9.42 kton-year for 9.3 to 30.8 MeV, we observe seven candidate events after applying a new deep-neural-network-based event classification technique. This result is consistent with the background-only expectation of 16.2 plus or minus 9.4 events, including 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 percent confidence-level upper limits on the DSNB flux of 38 to 43 per square centimeter per second, depending on the assumed DSNB model. We also derive model-independent 90 percent confidence-level upper limits on the electron-antineutrino flux, obtaining some of the most stringent constraints below 13.3 MeV. Beyond the DSNB search itself, this work demonstrates neural-network-based event classification as a promising approach for suppressing neutron-associated backgrounds in liquid-scintillator neutrino detectors.
ER  -