Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation
Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation
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DOI：
10.1088/1748-0221/17/01/p01037
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发表时间：
2021-10
影响因子：
1.3
通讯作者：
M. C. P. Abratenko;R. An;J. Anthony;L. Arellano;J. Asaadi;A. Ashkenazi;S. Balasubramanian;B. Baller;C. Barnes;G. Barr;V. Basque;L. Bathe-Peters;O. Rodrigues;S. Berkman;A. Bhanderi;A. Bhat;M. Bishai;A. Blake;T. Bolton;J. Book;L. Camilleri;D. Caratelli;I. C. Terrazas;R. C. Fernández;F. Cavanna;G. Cerati;Y. Chen;D. Cianci;J. Conrad;M. Convery;L. Cooper-Troendle;J. I. Crespo-Anadón;M. Tutto;S. Dennis;P. Detje;A. Devitt;R. Diurba;R. Dorrill;K. Duffy;S. Dytman;B. Eberly;A. Ereditato;J. Evans;R. Fine;G. A. F. Aguirre;R. Fitzpatrick;B. Fleming;N. Foppiani;D. Franco;A. Furmanski;D. Garcia-Gamez;S. Gardiner;G. Ge;S. Gollapinni;O. Goodwin;E. Gramellini;P. Green;H. Greenlee;W. Gu;R. Guenette;P. Guzowski;L. Hagaman;O. Hen;C. Hilgenberg;G. Horton-Smith;A. Hourlier;R. Itay;C. James;X. Ji;L. Jiang;J. H. Jo;R. Johnson;Y. Jwa;D. Kalra;N. Kamp;N. Kaneshige;G. Karagiorgi;W. Ketchum;M. Kirby;T. Kobilarcik;I. Kreslo;R. LaZur;I. Lepetic;K. Li;Y. Li;K. Lin;B. Littlejohn;W. Louis;X. Luo;K. Manivannan;C. Mariani;D. Marsden;J. Marshall;D. A. Caicedo;K. Mason;A. Mastbaum;N. McConkey;V. Meddage;T. Mettler;K. Miller;J. Mills;K. Mistry;T. Mohayai;A. Mogan;J. Moon;M. Mooney;A. Moor;C. Moore;L. Lepin;J. Mousseau;M. Murphy;D. Naples;A. Navrer-Agasson;M. Nebot-Guinot;R. Neely;D. Newmark;J. Nowak;M. Nunes;O. Palamara;V. Paolone;A. Papadopoulou;Vicky Papavassiliou;S. Pate;N. Patel;A. Paudel;Z. Pavlovic;E. Piasetzky;I. Ponce-Pinto;S. Prince;X. Qian;J. Raaf;V. Radeka;A. Rafique;M. Reggiani-Guzzo;L. Ren;L. Rice;L. Rochester;J. Rondon;M. Rosenberg;M. Ross-Lonergan;G. Scanavini;D. Schmitz;A. Schukraft;W. Seligman;M. Shaevitz;R. Sharankova;J. Shi;J. Sinclair;A. Smith;E. Snider;M. Soderberg;S. Soldner-Rembold;P. Spentzouris;J. Spitz;M. Stancari;J. John;T. Strauss;K. Sutton;S. Sword-Fehlberg;A. Szelc;W. Tang;K. Terao;C.Thorpe;D. Totani;M. Toups;Y. Tsai;M. Uchida;T. Usher;W. V. D. Pontseele;B. Viren;M. Weber;H. Wei;Z. Williams;S. Wolbers;T. Wongjirad;M. Wospakrik;K. Wresilo;N. Wright;W. Wu;E. Yandel;T. Yang;G. Yarbrough;L. Yates;H. Yu;G. Zeller;J. Zennamo;C. Zhang
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文献类型：
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作者：
M. C. P. Abratenko;R. An;J. Anthony;L. Arellano;J. Asaadi;A. Ashkenazi;S. Balasubramanian;B. Baller;C. Barnes;G. Barr;V. Basque;L. Bathe-Peters;O. Rodrigues;S. Berkman;A. Bhanderi;A. Bhat;M. Bishai;A. Blake;T. Bolton;J. Book;L. Camilleri;D. Caratelli;I. C. Terrazas;R. C. Fernández;F. Cavanna;G. Cerati;Y. Chen;D. Cianci;J. Conrad;M. Convery;L. Cooper-Troendle;J. I. Crespo-Anadón;M. Tutto;S. Dennis;P. Detje;A. Devitt;R. Diurba;R. Dorrill;K. Duffy;S. Dytman;B. Eberly;A. Ereditato;J. Evans;R. Fine;G. A. F. Aguirre;R. Fitzpatrick;B. Fleming;N. Foppiani;D. Franco;A. Furmanski;D. Garcia-Gamez;S. Gardiner;G. Ge;S. Gollapinni;O. Goodwin;E. Gramellini;P. Green;H. Greenlee;W. Gu;R. Guenette;P. Guzowski;L. Hagaman;O. Hen;C. Hilgenberg;G. Horton-Smith;A. Hourlier;R. Itay;C. James;X. Ji;L. Jiang;J. H. Jo;R. Johnson;Y. Jwa;D. Kalra;N. Kamp;N. Kaneshige;G. Karagiorgi;W. Ketchum;M. Kirby;T. Kobilarcik;I. Kreslo;R. LaZur;I. Lepetic;K. Li;Y. Li;K. Lin;B. Littlejohn;W. Louis;X. Luo;K. Manivannan;C. Mariani;D. Marsden;J. Marshall;D. A. Caicedo;K. Mason;A. Mastbaum;N. McConkey;V. Meddage;T. Mettler;K. Miller;J. Mills;K. Mistry;T. Mohayai;A. Mogan;J. Moon;M. Mooney;A. Moor;C. Moore;L. Lepin;J. Mousseau;M. Murphy;D. Naples;A. Navrer-Agasson;M. Nebot-Guinot;R. Neely;D. Newmark;J. Nowak;M. Nunes;O. Palamara;V. Paolone;A. Papadopoulou;Vicky Papavassiliou;S. Pate;N. Patel;A. Paudel;Z. Pavlovic;E. Piasetzky;I. Ponce-Pinto;S. Prince;X. Qian;J. Raaf;V. Radeka;A. Rafique;M. Reggiani-Guzzo;L. Ren;L. Rice;L. Rochester;J. Rondon;M. Rosenberg;M. Ross-Lonergan;G. Scanavini;D. Schmitz;A. Schukraft;W. Seligman;M. Shaevitz;R. Sharankova;J. Shi;J. Sinclair;A. Smith;E. Snider;M. Soderberg;S. Soldner-Rembold;P. Spentzouris;J. Spitz;M. Stancari;J. John;T. Strauss;K. Sutton;S. Sword-Fehlberg;A. Szelc;W. Tang;K. Terao;C.Thorpe;D. Totani;M. Toups;Y. Tsai;M. Uchida;T. Usher;W. V. D. Pontseele;B. Viren;M. Weber;H. Wei;Z. Williams;S. Wolbers;T. Wongjirad;M. Wospakrik;K. Wresilo;N. Wright;W. Wu;E. Yandel;T. Yang;G. Yarbrough;L. Yates;H. Yu;G. Zeller;J. Zennamo;C. Zhang
Wire-Cell 是液氩时间投影室的 3D 事件重建包。通过几何形状、时间和来自多个读出线平面的漂移电荷，在模式识别阶段之前重建具有相关电荷的 3D 空间点。然后将模式识别技术，包括轨迹轨迹和 dQ/dx（每单位长度的电离电荷）拟合、3D 中微子顶点拟合、轨迹和簇射分离、粒子级聚类和粒子识别应用于这些 3D 空间点以及原始 2D 投影测量。开发了深度神经网络来增强中微子相互作用顶点的重建。与传统算法相比，深度神经网络将充电电流 νe 交互的顶点效率提高了 30%。在带电电流 νe (νμ) 相互作用的顶点效率达到 65.8% (72.9%) 后，这种模式识别对初级轻子实现了 80-90% 的重建效率。基于所得的重建粒子及其运动学，我们还为带电中微子相互作用实现了 15-20% 的能量重建分辨率。
Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and dQ/dx (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30% for charged-current νe interactions. This pattern recognition achieves 80–90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current νe (νμ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions.