Details

Autor(en) / Beteiligte

• Abratenko, P.
• Asaadi, J.
• Ashkenazi, A.
• Balasubramanian, S.
• Baller, B.
• Barr, G.
• Barrow, J.
• Basque, V.
• Bhattacharya, M.
• Bishai, M.
• Blake, A.
• Bolton, T.
• Book, J. Y.
• Brunetti, M. B.
• Camilleri, L.
• Cao, Y.
• Cerati, G.
• Conrad, J. M.
• Cooper-Troendle, L.
• Del Tutto, M.
• Detje, P.
• Dorrill, R.
• Duffy, K.
• Dytman, S.
• Englezos, P.
• Ereditato, A.
• Fleming, B. T.
• Franco, D.
• Furmanski, A. P.
• Garcia-Gamez, D.
• Ge, G.
• Gollapinni, S.
• Greenlee, H.
• Gu, L.
• Guzowski, P.
• Hagaman, L.
• Hen, O.
• Hilgenberg, C.
• Imani, Z.
• Ismail, M. S.
• James, C.
• Ji, X.
• Kalra, D.
• Karagiorgi, G.
• Kobilarcik, T.
• Kreslo, I.
• Lepetic, I.
• Li, J.-Y.
• Li, K.
• Littlejohn, B. R.
• Louis, W. C.
• Luo, X.
• Mariani, C.
• Marsden, D.
• Martinez, N.
• Martinez Caicedo, D. A.
• Martynenko, S.
• Micallef, J.
• Mogan, A.
• Mohayai, T.
• Mooney, M.
• Moor, A. F.
• Naples, D.
• Navrer-Agasson, A.
• Nayak, N.
• Palamara, O.
• Papadopoulou, A.
• Papavassiliou, V.
• Parkinson, H. B.
• Pate, S. F.
• Pavlovic, Z.
• Radeka, V.
• Ross-Lonergan, M.
• Rudolf von Rohr, C.
• Schmitz, D. W.
• Schukraft, A.
• Sharankova, R.
• Shi, J.
• Soderberg, M.
• Söldner-Rembold, S.
• Spitz, J.
• Stancari, M.
• Tang, W.
• Taniuchi, N.
• Terao, K.
• Torbunov, D.
• Totani, D.
• Toups, M.
• Tsai, Y.-T.
• Tyler, J.
• Usher, T.
• Viren, B.
• Wei, H.
• Wolbers, S.
• Wresilo, K.
• Wu, W.
• Yang, T.
• Yates, L. E.
• Yu, H. W.
• Zhang, C.

Titel

First Search for Dark-Trident Processes Using the MicroBooNE Detector

Ist Teil von

• Physical review letters, 2024-06, Vol.132 (24), p.241801, Article 241801

Ort / Verlag

United States: American Physical Society (APS)

Erscheinungsjahr

2024

Quelle

American Physical Society

Beschreibungen/Notizen

• We present a first search for dark-trident scattering in a neutrino beam using a dataset corresponding to 7.2 × 10 20 protons on target taken with the MicroBooNE detector at Fermilab. Proton interactions in the neutrino target at the main injector produce π 0 and η mesons, which could decay into dark-matter (DM) particles mediated via a dark photon A ′ . A convolutional neural network is trained to identify interactions of the DM particles in the liquid-argon time projection chamber (LArTPC) exploiting its imagelike reconstruction capability. In the absence of a DM signal, we provide limits at the 90% confidence level on the squared kinematic mixing parameter ϵ 2 as a function of the dark-photon mass in the range 10 ≤ M A ′ ≤ 400 MeV . The limits cover previously unconstrained parameter space for the production of fermion or scalar DM particles χ for two benchmark models with mass ratios M χ / M A ′ = 0.6 and 2 and for dark fine-structure constants 0.1 ≤ α D ≤ 1 . Published by the American Physical Society 2024