在微酮液体氩时间投影室内用于多个粒子鉴定的卷积神经网络

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在微酮液体氩时间投影室内用于多个粒子鉴定的卷积神经网络

A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

论文作者

MicroBooNE collaboration, Abratenko, P., Alrashed, M., An, R., Anthony, J., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Basque, V., Bathe-Peters, L., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Camilleri, L., Caratelli, D., Terrazas, I. Caro, Fernandez, R. Castillo, Cavanna, F., Cerati, G., Chen, Y., Church, E., Cianci, D., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Del Tutto, M., Dennis, S., Devitt, D., Diurba, R., Domine, L., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Sanchez, L. Escudero, Evans, J. J., Aguirre, G. A. Fiorentini, Fitzpatrick, R. S., Fleming, B. T., Foppiani, N., Franco, D., Furmanski, A. P., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Goodwin, O., Gramellini, E., Green, P., Greenlee, H., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hall, E., Hamilton, P., Hen, O., Horton-Smith, G. A., Hourlier, A., Itay, R., James, C., de Vries, J. Jan, Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kamp, N., Kaneshige, N., Karagiorgi, G., Ketchum, W., Kirby, B., Kirby, M., Kobilarcik, T., Kreslo, I., LaZur, R., Lepetic, I., Li, K., Li, Y., Littlejohn, B. R., Lorca, D., Louis, W. C., Luo, X., Marchionni, A., Mariani, C., Marsden, D., Marshall, J., Martin-Albo, J., Caicedo, D. A. Martinez, Mason, K., Mastbaum, A., McConkey, N., Meddage, V., Mettler, T., Miller, K., Mills, J., Mistry, K., Mohayai, T., Mogan, A., Moon, J., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Mousseau, J., Murphy, M., Naples, D., Navrer-Agasson, A., Neely, R. K., Nienaber, P., Nowak, J., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I., Porzio, D., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rogers, H. E., Rosenberg, M., Ross-Lonergan, M., Russell, B., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Soleti, S. R., Spentzouris, P., Spitz, J., Stancari, M., John, J. St., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tagg, N., Tang, W., Terao, K., Thorpe, C., Toups, M., Tsai, Y. -T., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wu, W., Yandel, E., Yang, T., Yarbrough, G., Yates, L. E., Zeller, G. P., Zennamo, J., Zhang, C.

论文摘要

我们介绍了多个粒子识别（MPID）网络，这是一种用于多个对象分类的卷积神经网络（CNN），由Microboone开发。 MPID提供$ E^ - $，$γ$，$μ^ - $，$π^\ pm $的概率和单个液体氩时间投影室（LARTPC）读数平面中的质子。该网络扩展了先前由微酮开发的单个粒子识别网络。 MPID作为输入作为输入的图像，要么围绕重建的相互作用顶点裁剪，要么仅包含连接到重建的顶点的活动，因此将工具从顶点查找和粒子聚类中的效率低下缓解。该网络是Microboone基于深度学习的$ν_e$搜索分析的重要组成部分。在本文中，我们介绍了网络的设计，培训和性能，以模拟和来自微生物检测器的数据。

We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of $e^-$, $γ$, $μ^-$, $π^\pm$, and protons in a single liquid argon time projection chamber (LArTPC) readout plane. The network extends the single particle identification network previously developed by MicroBooNE. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep learning based $ν_e$ search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.

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