Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 26 min ago
Peer-reviewedCC BY 4.0The Astrophysical Journal

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

  • D. Chernyak1
  • T. Eda1
  • M. Eizuka1
  • R. Endo1
  • A. Gando1,2
  • Y. Gando1,2
  • T. Hachiya1
  • F. Haneishi1
  • K. Hata1
  • T. Hirai1
  • K. Hosokawa1
  • K. Ichimura1
62 more authors
  • H. Ikeda1
  • K. Inoue1
  • K. Ishidoshiro1
  • Y. Kamei1
  • N. Kawada1
  • Y. Kishimoto1
  • M. Koga1,3
  • K. Mikami1
  • H. Miyake1
  • K. Mizukoshi1
  • D. Morita1
  • K. Nakamura1
  • R. Nakamura1
  • J. Nakane1
  • Y. Nakano1
  • N. Obata1
  • Y. Ota1
  • K. Saito1
  • I. Sakaki1
  • I. Shimizu1
  • J. Shirai1
  • A. Suzuki1
  • K. Tachibana1
  • A. Takeuchi1
  • K. Tamae1
  • H. Watanabe1
  • Z. Xu1
  • S. Yoshida4
  • S. Umehara4
  • K. Fushimi5
  • K. Kotera5
  • Y. Urano5
  • S. Kurosawa6
  • H. Ozaki7
  • B.E. Berger8
  • B.K. Fujikawa3,8
  • J.G. Learned9
  • J. Maricic9
  • Z. Li9
  • Z. Fu10
  • S. Ghosh10
  • J. Smolsky10
  • L.A. Winslow10
  • Y. Efremenko3,11
  • H.J. Karwowski12,13,14,15
  • D.M. Markoff12,13,14,15
  • W. Tornow3,12,13,14,15
  • S. Dell’Oro16

60 listed; 74 in total.

16 affiliations
  1. 1Tohoku University
  2. 2Obihiro University of Agriculture and Veterinary Medicine
  3. 3The University of Tokyo
  4. 4Osaka University
  5. 5Tokushima University
  6. 6Nagoya University
  7. 7Tokyo University of Science
  8. 8Lawrence Berkeley National Laboratory
  9. 9University of Hawai'i at Manoa
  10. 10Massachusetts Institute of Technology
  11. 11University of Tennessee
  12. 12Triangle Universities Nuclear Laboratory
  13. 13Physics Department at Duke University
  14. 14North Carolina Central University
  15. 15The University of North Carolina at Chapel Hill
  16. 16Virginia Polytechnic Institute and State University
Open at sourcePDFDOICite

Abstract

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.

Related

  1. Probing Scalar-Induced First-Order Phase Transitions with the Diffuse Supernova Neutrino Background
    Sudipta Das, Shamik Niyogi, Manibrata Sen
    arXivPreprintCC BY 4.0Full textCosmologyHigh-energy & particle physicsarXiv
  2. A Two-Parameter Framework for the Diffuse Supernova Neutrino Background
    Cecilia Lunardini, Xingyun Yang
    arXivPreprintarXiv licenceFull textHigh-energy astrophysicsHigh-energy & particle physicsarXiv
  3. Indication of Electron Antineutrinos from the Diffuse Supernova Neutrino Background
    Kamiokande Collaboration, K. Abe, Y. Asaoka +248
    arXivPreprintCC BY 4.0Full textHigh-energy astrophysicsHigh-energy & particle physicsarXiv
  4. Diffuse Supernova Neutrinos with Secret Neutrino Interactions
    Praveen Bharadwaj, Utpal Chattopadhyay, Dilip Kumar Ghosh +1
    arXivPreprintarXiv licenceFull textHigh-energy astrophysicsHigh-energy & particle physicsarXiv
  5. Exploring neutrino loss with diffuse astrophysical neutrino fluxes
    Ivan Esteban, Alberto M. Gago, M. C. Gonzalez-Garcia +1
    arXivPreprintarXiv licenceFull textCosmologyHigh-energy astrophysicsarXiv

Cite

BibTeXRIS
BibTeX
@article{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 Z. Fu and S. Ghosh and J. Smolsky and L.A. Winslow and Y. Efremenko and H.J. Karwowski and D.M. Markoff and W. Tornow and S. Dell’Oro},
  journal = {The Astrophysical Journal},
  year = {2026},
  month = nov,
  doi = {10.3847/1538-4357/aeb2ea},
  url = {https://iopscience.iop.org/article/10.3847/1538-4357/aeb2ea},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
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  - Z. Fu
AU  - S. Ghosh
AU  - J. Smolsky
AU  - L.A. Winslow
AU  - Y. Efremenko
AU  - H.J. Karwowski
AU  - D.M. Markoff
AU  - W. Tornow
AU  - S. Dell’Oro
PY  - 2026
DA  - 2026/11/05
JO  - The Astrophysical Journal
DO  - 10.3847/1538-4357/aeb2ea
UR  - https://iopscience.iop.org/article/10.3847/1538-4357/aeb2ea
AB  - 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.
SN  - 1538-4357
SN  - 0004-637X
ER  -