Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN

Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN
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DOI：
10.1088/1748-0221/17/09/p09015
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发表时间：
2022-01
期刊：
Journal of Instrumentation
影响因子：
1.3
通讯作者：
M. C. P. Abratenko;Rui An;J. Anthony;L. Arellano;J. Asaadi;A. Ashkenazi;S. Balasubramanian;B. Baller;C. Barnes;G. Barr;J. Barrow;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;F. Cavanna;G. Cerati;Y. Chen;E. Church;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. Jo;R. Johnson;Y. Jwa;D. Kalra;N. Kamp;N. Kaneshige;G. Karagiorgi;W. Ketchum;M. Kirby;T. Kobilarcik;I. Kreslo;I. Lepetic;J.-Y. Li;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;S. Babu;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;F. Yu;H. Yu;G. Zeller;J. Zennamo;C. Zhang
M. C. P. Abratenko;Rui An;J. Anthony;L. Arellano;J. Asaadi;A. Ashkenazi;S. Balasubramanian;B. Baller;C. Barnes;G. Barr;J. Barrow;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;F. Cavanna;G. Cerati;Y. Chen;E. Church;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. Jo;R. Johnson;Y. Jwa;D. Kalra;N. Kamp;N. Kaneshige;G. Karagiorgi;W. Ketchum;M. Kirby;T. Kobilarcik;I. Kreslo;I. Lepetic;J.-Y. Li;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;S. Babu;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;F. Yu;H. Yu;G. Zeller;J. Zennamo;C. Zhang
中科院分区：
工程技术4区
文献类型：
--
作者：
M. C. P. Abratenko;Rui An;J. Anthony;L. Arellano;J. Asaadi;A. Ashkenazi;S. Balasubramanian;B. Baller;C. Barnes;G. Barr;J. Barrow;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;F. Cavanna;G. Cerati;Y. Chen;E. Church;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. Jo;R. Johnson;Y. Jwa;D. Kalra;N. Kamp;N. Kaneshige;G. Karagiorgi;W. Ketchum;M. Kirby;T. Kobilarcik;I. Kreslo;I. Lepetic;J.-Y. Li;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;S. Babu;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;F. Yu;H. Yu;G. Zeller;J. Zennamo;C. Zhang

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在这篇文章中，我们描述了一个修改后的实施掩模区域为基础的卷积神经网络（Mask-RCNN）的宇宙射线μ子集群在液态氩TPC和应用到MicroBooNE中微子数据。我们对这个网络的实现称为sMask-RCNN，它使用稀疏子流形卷积来提高稀疏数据集的处理速度，并在几个指标上与原始密集版本进行了比较。网络被训练为使用来自MicroBooNE液氩时间投影室的线读出图像作为输入，并在图像内产生单独标记的粒子相互作用。这些输出被确定为宇宙射线μ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%，而密集网络的平均像素聚类效率为89.1%。我们证明了sMask-RCNN与MicroBooNE最先进的Wire-Cell宇宙标记器结合使用的能力，以否决仅包含宇宙射线μ子的事件。将sMask-RCNN添加到Wire-Cell宇宙标记器中，以相同的电子中微子事件信号效率去除了70%的剩余宇宙射线μ子背景事件。这种事件否决可以提供99.7%的宇宙射线背景事件的拒绝，同时保持80.1%的电子中微子事件级信号效率。除了宇宙射线μ子识别之外，sMask-RCNN还可以用于提取特征并识别其他3D跟踪探测器中的不同粒子相互作用类型。
In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. This event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition to cosmic ray muon identification, sMask-RCNN could be used to extract features and identify different particle interaction types in other 3D-tracking detectors.