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Measurement of reconstructed final-state kinematics in charged-current interactions on water using the J-PARC νμ beam and NINJA emulsion detector

NINJA Collaboration, A. Kasumi, S. Han, E. Abad Díaz, S. Aoki, D. Barčot, C. Bronner, T. Fukuda, Y. Furuta, M. Ghosh, T. Hayakawa, Y. Hayasaka, Y. Hayato, L. Halić, D. Hirata, H. Hotta, M. Jakkapu, C. Jesús-Valls, T. Katori, H. Kawahara, T. Kawahara, T. Kikawa, B. Kilček, H. Kobayashi, R. Komatani, M. Komatsu, B. Kovač, T. Matsuo, S. Mikado, A. Minamino, K. Mizuno, Y. Morimoto, K. Morishima, Y. Nakamura, T. Nakano, T. Nakaya, T. Nishikiori, H. Oaira, T. Odagawa, S. Ogawa, H. Oshima, N. Otani, G. Pintaudi, H. Rokujo, O. Sato, H. Shibuya, S. Shimizu, K. Sugimura, Y. Suzuki, S. Takeshita, K. Yasutome, S. Yamamoto, M. Yoshimoto

hep-exarXiv:2608.16063

Abstract

We present a study of charged-particle multiplicities and the kinematic distributions of muons, protons, and charged pions in a flux-integrated sample of charged-current inclusive νμ interactions on water recorded with the NINJA nuclear emulsion detector exposed to the J-PARC νμ-focused beam. The data correspond to an exposure of 4.63×1020 protons on target, with a neutrino energy spectrum peaked at 0.7 GeV. In this analysis, 82 events are selected from a fiducial water volume with a mass of 3.9 kg, corresponding to 5% of the total water target mass exposed during the run. The reconstructed distributions are compared with Monte Carlo predictions based on the interaction model used in the T2K experiment. The observed distributions are generally consistent with the predictions within the estimated uncertainties, although the proton angular distribution exhibits some tension with the prediction. The results are currently limited by statistical uncertainties. This analysis provides the first study of proton kinematics with sensitivity to proton momenta as low as 200 MeV/c with a νμ-focused beam on a water target using nuclear emulsion data and establishes the analysis framework for future measurements with substantially larger NINJA data samples.

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