用于大型 LArTPC 中微子事件重建的 Wire-Cell 三维模式识别技术:算法描述与 MicroBooNE 模拟的定量评估
仪器与探测器
2022-04-07 v3 高能物理 - 实验
摘要
Wire-Cell 是用于液氩时间投影室的 3D 事件重建软件包。通过几何、时间以及来自多个读出丝平面的漂移电荷,在模式识别阶段之前重建出带关联电荷的 3D 空间点。随后在这些 3D 空间点以及原始 2D 投影测量上应用模式识别技术,包括径迹轨迹与 (单位长度电离电荷)拟合、3D 中微子顶点拟合、径迹与簇射分离、粒子级聚类以及粒子鉴别。我们开发了深度神经网络以增强中微子相互作用顶点的重建。相比传统算法,该深度神经网络将带电电流 相互作用的顶点效率相对提升了 30%。该模式识别对初级轻子实现 80-90% 的重建效率,此前带电电流 () 相互作用的顶点效率为 65.8% (72.9%)。基于所得重建粒子及其运动学,我们还对带电电流中微子相互作用实现了 15-20% 的能量重建分辨率。
引用
@article{arxiv.2110.13961,
title = {Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation},
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 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 R. Castillo Fernandez and F. Cavanna and G. Cerati and Y. Chen 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 R. LaZur and I. Lepetic 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 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 H. W. Yu and G. P. Zeller and J. Zennamo and C. Zhang},
journal= {arXiv preprint arXiv:2110.13961},
year = {2022}
}