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◆ The Astrophysical Journal2026-06-29· Supernova

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

K. Abe, S. Abe, Y Asaoka, M. Harada, Y. Hayato, K Hiraide, K Hosokawa, Tzu-Chiao Hung, K. Ieki, M. Ikeda, J. Kameda, Y. Kanemura, Y. Kataoka, S. Miki, S. Mine, M. Miura, S Moriyama, M. Nakahata, S. Nakayama, Yuji Noguchi, G. Pronost, Y. Satou, H. Sekiya, R Shinoda, M. Shiozawa, Y. Suzuki, A. Takeda, Yasuhiko Takemoto, H. Tanaka, T. Yano, Y. Itow, T. Kajita, Ryo Nishijima, Keisuke Okumura, T Tashiro, Tomoaki Tomiya, X. Wang, P. Fernández, L. Labarga, D Samudio, B. Zaldivar, C. Yanagisawa, E. Kearns, J. Mirabito, L. Wan, T. Wester, B. W. Pointon, J. Bian, B. Cortez, N.J Griskevich, Y. Jiang, M. Smy, H. W. Sobel, V. Takhistov, A. Yankelevich, J. Hill, M. C. Jang, S. H. Lee, D. H. Moon, R.G Park, B. S. Yang, B. Bodur, K. Scholberg, C. W. Walter, A Beauchene, E Le Blévec, O. Drapier, A. Ershova, M. Ferey, Th. A. Mueller, A. D. Santos, Pascal Paganini, Caroline Quach, R. Rogly, Takuji Nakamura, J.S Jang, R. P. Litchfield, L. N. Machado, F. J. P. Soler, J. G. Learned, K. Choi, S. Cao, L. H. V. Anthony, N. W. Prouse, M. Scott, Y. Uchida, V. Berardi, N.F Calabria, M G Catanesi, N. Ospina, E. Radicioni, A. Langella, G. De Rosa, G. Collazuol, M. Feltre, M. Mattiazzi, L. Ludovici, M. Gonin, Lorenzo Périssé, B. Quilain

原始摘要(英文原文)· Original abstract
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 a 22,500 × 956.2 m 3 day exposure. Starting in the summer of 2020, SK introduced 0.01% gadolinium (Gd) by mass into its ultrapure 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 2023 September. 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% confidence level (C.L.) upper limits on the astrophysical electron antineutrino flux were set. Additionally, a spectral fitting result exhibited a ∼1.2 σ disagreement with a null DSNB hypothesis, comparable to a previous result from 5823 days of all SK pure-water phases.
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