Abratenko P, Alterkait O, Andrade Aldana D, Arellano L, Asaadi J, Ashkenazi A, Balasubramanian S, Baller B, Barr G, Barrow D, Barrow J, Basque V, Benevides Rodrigues O, Berkman S, Bhanderi A, Bhat A, Bhattacharya M, Bishai M, Blake A, Bogart B, Bolton T, Book J Y, Brunetti M B, Camilleri L, Cao Y, Caratelli D, Cavanna F, Cerati G, Chappell A, Chen Y, Conrad J M, Convery M, Cooper-Troendle L, Crespo-Anadón J I, Cross R, Del Tutto M, Dennis S R, Detje P, Devitt A, Diurba R, Djurcic Z, Dorrill R, Duffy K, Dytman S, Eberly B, Englezos P, Ereditato A, Evans J J, Fine R, Finnerud O G, Foreman W, Fleming B T, Franco D, Furmanski A P, Gao F, Garcia-Gamez D, Gardiner S, Ge G, Gollapinni S, Gramellini E, Green P, Greenlee H, Gu L, Gu W, Guenette R, Guzowski P, Hagaman L, Hen O, Hilgenberg C, Horton-Smith G A, Imani Z, Irwin B, Ismail M S, James C, Ji X, Jo J H, Johnson R A, Jwa Y-J, Kalra D, Kamp N, Karagiorgi G, Ketchum W, Kirby M, Kobilarcik T, Kreslo I, Leibovitch M B, Lepetic I, Li J-Y, Li K, Li Y, Lin K, Littlejohn B R, Liu H, Louis W C, Luo X, Mariani C, Marsden D, Marshall J, Martinez N, Martinez Caicedo D A, Martynenko S, Mastbaum A, Mawby I, McConkey N, Meddage V, Micallef J, Miller K, Mogan A, Mohayai T, Mooney M, Moor A F, Moore C D, Mora Lepin L, Moudgalya M M, Mulleriababu S, Naples D, Navrer-Agasson A, Nayak N, Nebot-Guinot M, Nowak J, Oza N, Palamara O, Pallat N, Paolone V, Papadopoulou A, Papavassiliou V, Parkinson H B, Pate S F, Patel N, Pavlovic Z, Piasetzky E, Pophale I, Qian X, Raaf J L, Radeka V, Rafique A, Reggiani-Guzzo M, Ren L, Rochester L, Rodriguez Rondon J, Rosenberg M, Ross-Lonergan M, Rudolf von Rohr C, Safa I, Scanavini G, Schmitz D W, Schukraft A, Seligman W, Shaevitz M H, Sharankova R, Shi J, Snider E L, Soderberg M, Söldner-Rembold S, Spitz J, Stancari M, John J St, Strauss T, Szelc A M, Tang W, Taniuchi N, Terao K, Thorpe C, Torbunov D, Totani D, Toups M, Tsai Y-T, Tyler J, Uchida M A, Usher T, Viren B, Weber M, Wei H, White A J, Wolbers S, Wongjirad T, Wospakrik M, Wresilo K, Wu W, Yandel E, Yang T, Yates L E, Yu H W, Zeller G P, Zennamo J, Zhang C
Tufts University, Medford, Massachusetts 02155, USA.
Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA.
Phys Rev Lett. 2024 Jun 14;132(24):241801. doi: 10.1103/PhysRevLett.132.241801.
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.
我们利用费米实验室的MicroBooNE探测器获取的对应于7.2×10²⁰个靶上质子的数据集，首次对中微子束中的暗三叉戟散射进行了搜索。主注入器中微子靶的质子相互作用产生π⁰和η介子，它们可能衰变成通过暗光子A′介导的暗物质（DM）粒子。训练了一个卷积神经网络，利用其类似图像的重建能力来识别液氩时间投影室（LArTPC）中DM粒子的相互作用。在没有DM信号的情况下，我们给出了90%置信水平下平方运动学混合参数ϵ²作为暗光子质量在10≤Mₐ′≤400 MeV范围内的函数的限制。这些限制涵盖了质量比Mχ/Mₐ′ = 0.6和2的两个基准模型以及暗精细结构常数0.1≤αD≤1时，费米子或标量DM粒子χ产生的先前未受约束的参数空间。
