基于 sMask-RCNN 的 MicroBooNE 液态氩时间投影室宇宙线缪子聚类
高能物理 - 实验
2022-05-25 v3
摘要
在本文中，我们描述了一种改进的 Mask Region-based Convolutional Neural Networks（Mask-RCNN）实现，用于液态氩 TPC 中的宇宙线缪子聚类，并应用于 MicroBooNE 中微子数据。我们对这一网络的实现称为 sMask-RCNN，其使用稀疏子流形卷积（sparse submanifold convolutions）来提高稀疏数据集上的处理速度，并在多项指标上与原始稠密版本进行比较。这些网络以来自 MicroBooNE 液态氩时间投影室的丝读数图像（wire readout images）作为输入，并在图像内产生单独标记的粒子相互作用。这些输出被识别为宇宙线缪子或电子中微子相互作用。我们发现 sMask-RCNN 的平均像素聚类效率为 85.9%，而稠密网络的平均像素聚类效率为 89.1%。我们展示了 sMask-RCNN 与 MicroBooNE 最先进的 Wire-Cell 宇宙线标记器（cosmic tagger）结合使用以 veto 仅含宇宙线缪子的事件的能力。在相同的电子中微子事件信号效率下，向 Wire-Cell 宇宙线标记器添加 sMask-RCNN 可去除剩余宇宙线缪子本底事件的 70%。该事件 veto 可在保持 80.1% 电子中微子事件级信号效率的同时，提供 99.7% 的宇宙线-only 本底事件抑制率。除宇宙线缪子识别外，sMask-RCNN 还可用于提取特征并识别其他 3D 跟踪探测器中不同类型的粒子相互作用。
引用
@article{arxiv.2201.05705,
title = {Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN},
author = {MicroBooNE collaboration and P. Abratenko and R. An and J. Anthony and L. Arellano and J. Asaadi and A. Ashkenazi and S. Balasubramanian and B. Baller and C. Barnes and G. Barr and J. Barrow and V. Basque and L. Bathe-Peters and O. Benevides Rodrigues and S. Berkman and A. Bhanderi and A. Bhat and M. Bishai and A. Blake and T. Bolton and J. Y. Book and L. Camilleri and D. Caratelli and I. Caro Terrazas and F. Cavanna and G. Cerati and Y. Chen and E. Church and D. Cianci and J. M. Conrad and M. Convery and L. Cooper-Troendle and J. I. Crespo-Anadon and M. Del Tutto and S. R. Dennis and P. Detje and A. Devitt and R. Diurba and R. Dorrill and K. Duffy and S. Dytman and B. Eberly and A. Ereditato and J. J. Evans and R. Fine and G. A. Fiorentini Aguirre and R. S. Fitzpatrick and B. T. Fleming and N. Foppiani and D. Franco and A. P. Furmanski and D. Garcia-Gamez and S. Gardiner and G. Ge and S. Gollapinni and O. Goodwin and E. Gramellini and P. Green and H. Greenlee and W. Gu and R. Guenette and P. Guzowski and L. Hagaman and O. Hen and C. Hilgenberg and G. A. Horton-Smith and A. Hourlier and R. Itay and C. James and X. Ji and L. Jiang and J. H. Jo and R. A. Johnson and Y. J. Jwa and D. Kalra and N. Kamp and N. Kaneshige and G. Karagiorgi and W. Ketchum and M. Kirby and T. Kobilarcik and I. Kreslo and I. Lepetic and J. -Y. Li and K. Li and Y. Li and K. Lin and B. R. Littlejohn and W. C. Louis and X. Luo and K. Manivannan and C. Mariani and D. Marsden and J. Marshall and D. A. Martinez Caicedo and K. Mason and A. Mastbaum and N. McConkey and V. Meddage and T. Mettler and K. Miller and J. Mills and K. Mistry and T. Mohayai and A. Mogan and J. Moon and M. Mooney and A. F. Moor and C. D. Moore and L. Mora Lepin and J. Mousseau and S. Mulleria Babu and M. Murphy and D. Naples and A. Navrer-Agasson and M. Nebot-Guinot and R. K. Neely and D. A. Newmark and J. Nowak and M. Nunes and O. Palamara and V. Paolone and A. Papadopoulou and V. Papavassiliou and S. F. Pate and N. Patel and A. Paudel and Z. Pavlovic and E. Piasetzky and I. Ponce-Pinto and S. Prince and X. Qian and J. L. Raaf and V. Radeka and A. Rafique and M. Reggiani-Guzzo and L. Ren and L. C. J. Rice and L. Rochester and J. Rodriguez Rondon and M. Rosenberg and M. Ross-Lonergan and G. Scanavini and D. W. Schmitz and A. Schukraft and W. Seligman and M. H. Shaevitz and R. Sharankova and J. Shi and J. Sinclair and A. Smith and E. L. Snider and M. Soderberg and S. Soldner-Rembold and P. Spentzouris and J. Spitz and M. Stancari and J. St. John and T. Strauss and K. Sutton and S. Sword-Fehlberg and A. M. Szelc and W. Tang and K. Terao and C. Thorpe and D. Totani and M. Toups and Y. -T. Tsai and M. A. Uchida and T. Usher and W. Van De Pontseele and B. Viren and M. Weber and H. Wei and Z. Williams and S. Wolbers and T. Wongjirad and M. Wospakrik and K. Wresilo and N. Wright and W. Wu and E. Yandel and T. Yang and G. Yarbrough and L. E. Yates and F. J. Yu and H. W. Yu and G. P. Zeller and J. Zennamo and C. Zhang},
journal= {arXiv preprint arXiv:2201.05705},
year = {2022}
}
备注
30 pages, 21 figures