SBND中的深层神经网络删除宇宙背景

论文标题

SBND中的深层神经网络删除宇宙背景

Cosmic Background Removal with Deep Neural Networks in SBND

论文作者

SBND Collaboration, Acciarri, R., Adams, C., Andreopoulos, C., Asaadi, J., Babicz, M., Backhouse, C., Badgett, W., Bagby, L., Barker, D., Basque, V., Bazetto, M. C. Q., Betancourt, M., Bhanderi, A., Bhat, A., Bonifazi, C., Brailsford, D., Brandt, A. G., Brooks, T., Carneiro, M. F., Chen, Y., Chen, H., Chisnall, G., Crespo-Anadón, J. I., Cristaldo, E., Cuesta, C., Astiz, I. L. de Icaza, De Roeck, A., Pereira, G. de Sá, Del Tutto, M., Di Benedetto, V., Ereditato, A., Evans, J. J., Ezeribe, A. C., Fitzpatrick, R. S., Fleming, B. T., Foreman, W., Franco, D., Furic, I., Furmanski, A. P., Gao, S., Garcia-Gamez, D., Frandini, H., Ge, G., Gil-Botella, I., Gollapinni, S., Goodwin, O., Green, P., Griffith, W. C., Guenette, R., Guzowski, P., Ham, T., Henzerling, J., Holin, A., Howard, B., Jones, R. S., Kalra, D., Karagiorgi, G., Kashur, L., Ketchum, W., Kim, M. J., Kudryavtsev, V. A., Larkin, J., Lay, H., Lepetic, I., Littlejohn, B. R., Louis, W. C., Machado, A. A., Malek, M., Mardsen, D., Mariani, C., Marinho, F., Mastbaum, A., Mavrokoridis, K., McConkey, N., Meddage, V., Méndez, D. P., Mettler, T., Mistry, K., Mogan, A., Molina, J., Mooney, M., Mora, L., Moura, C. A., Mousseau, J., Navrer-Agasson, A., Nicolas-Arnaldos, F. J., Nowak, J. A., Palamara, O., Pandey, V., Pater, J., Paulucci, L., Pimentel, V. L., Psihas, F., Putnam, G., Qian, X., Raguzin, E., Ray, H., Reggiani-Guzzo, M., Rivera, D., Roda, M., Ross-Lonergan, M., Scanavini, G., Scarff, A., Schmitz, D. W., Schukraft, A., Segreto, E., Nunes, M. Soares, Soderberg, M., Söldner-Rembold, S., Spitz, J., Spooner, N. J. C., Stancari, M., Stenico, G. V., Szelc, A., Tang, W., Vidal, J. Tena, Torretta, D., Toups, M., Touramanis, C., Tripathi, M., Tufanli, S., Tyley, E., Valdiviesso, G. A., Worcester, E., Worcester, M., Yarbrough, G., Yu, J., Zamorano, B., Zennamo, J., Zglam, A.

论文摘要

在液体氩期的时间投影室中，暴露于中微子束并在表面水平上运行或接近表面水平，宇宙muons和其他宇宙颗粒入射在探测器上，同时记录了一个中微子引起的事件。在实践中，这意味着来自表面液体氩时间投影室的数据将由宇宙颗粒（无论是事件触发器的来源）所主导，并且是真正中微子触发的事件中的大部分粒子数量。在这项工作中，我们通过对SBND检测器的完整检测器图像应用语义分割来展示深度学习技术的新应用来消除这些背景粒子，这是Fermilab短基线中微子计划中的近检测器。我们使用此技术在单像像素水平上识别记录的活动是源自宇宙颗粒还是中微子相互作用。

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying semantic segmentation on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, at single image-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

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