Vertex-finding and reconstruction of contained two-track neutrino events in the MicroBooNE detector
P. Abratenko,M. Alrashed,R. An,J. Anthony,J. Asaadi,Adi Ashkenazi,S. Balasubramanian,B. Baller,C. Barnes,G.D. Barr,V. Basque,L. Bathe-Peters,S. Berkman,A. Bhanderi,A. Bhat,M. Bishai,Andrew Blake,T. Bolton,L. Camilleri,D. Caratelli,I. Caro Terrazas,R. Castillo Fernandez,F. Cavanna,Giuseppe Benedetto Cerati,Yi Chen,E. Church,D. Cianci,Eliahu Cohen,Janet Conrad,M. E. Convery,L. Cooper-Troendle,M. Del Tutto,D. Devitt,Laura Dominé,K. E. Duffy,S. Dytman,B. Eberly,Antonio Ereditato,L. Escudero Sanchez,John Evans,G. A. Fiorentini Aguirre,R. S. Fitzpatrick,B.T. Fleming,N. Foppiani,D. Franco,A. P. Furmanski,D. Garcia-Gamez,Steven Gardiner,V. Genty,D. Goeldi,S. Gollapinni,O. Goodwin,E. Gramellini,Paul J. Green,H. Greenlee,L. Gu,W. Q. Gu,R. Guenette,P. Guzowski,E. D. Hall,P. M. Hamilton,Or Hen,C. S. Hill,G. A. Horton-Smith,A. Hourlier,E.-C. Huang,R. Itay,C. James,J. Jan de Vries,Xiaolu Ji,L. Jiang,J. H. Jo,R. A. Johnson,Y.-J. Jwa,G. Karagiorgi,W. Ketchum,B. Kirby,Michael H Kirby,T. Kobilarcik,I. Kreslo,R. LaZur,I. Lepetic,K. Li,Yang Li,Alison Lister,B. R. Littlejohn,S. Lockwitz,D. Lorca,W. C. Louis,M. Luethi,B. Lundberg,X. Luo,A. Marchionni,S. Marcocci,C. Mariani,John Marshall,J. Martin-Albo,D. A. Martinez Caicedo,K. Mason,A. Mastbaum,N. McConkey,V. Meddage,T. Mettler,Kate C. Miller,J. C. Mills,K. P. Mistry,A. Mogan,T. A. Mohayai,J. Moon,M. Mooney,C.D. Moore,J. Mousseau,Michael T. Murphy,D. Naples,R. K. Neely,P. Nienaber,J. A. Nowak,O. Palamara,Vishvas Pandey,V. Paolone,Afroditi Papadopoulou,V. Papavassiliou,S. F. Pate,A. Paudel,Z. Pavlovic,E. Piasetzky,I. Ponce-Pinto,D. Porzio,Sebastien Prince,G. Pulliam,Xin Qian,J. L. Raaf,Veljko Radeka,A. Rafique,L. Ren,L.S. Rochester,J. Rodriguez Rondon,H. E. Rogers,M. Ross-Lonergan,C. Rudolf von Rohr,B. Russell,G. Scanavini,D.W. Schmitz,A. Schukraft,W. G. Seligman,M. H. Shaevitz,R. Sharankova,J. R. Sinclair,A. M. Smith,E.L. Snider,M. Soderberg,S. Söldner-Rembold,S.R. Soleti,Panagiotis Spentzouris,J. Spitz,M. Stancari,J. St. John,Thomas Strauss,K. Sutton,S. Sword-Fehlberg,A. M. Szelc,N. Tagg,William Tang,Kazuhiro Terao,Rod Thornton,M. Toups,Y. T. Tsai,Serhan Tufanli,M. A. Uchida,T. L. Usher,W. Van De Pontseele,R. G. Van de Water,B. Viren,M. Weber,H. Y. Wei,D. A. Wickremasinghe,Z. Williams,S. Wolbers,T. Wongjirad,M. Wospakrik,Wei Wu,T. Yang,G. Yarbrough,L.E. Yates,G. P. Zeller,J. Zennamo,Chao Zhang +186 more
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TL;DR: In this article, the authors describe algorithms developed to isolate and accurately reconstruct two-track events that are contained within the MicroBooNE detector, which can be applied to searches for neutrino oscillations and measurements of cross sections using quasi-elastic-like charged current events.
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Abstract: We describe algorithms developed to isolate and accurately reconstruct two-track events that are contained within the MicroBooNE detector. This method is optimized to reconstruct two tracks of lengths longer than \SI{5}{\centi\meter}. This code has applications to searches for neutrino oscillations and measurements of cross sections using quasi-elastic-like charged current events. The algorithms we discuss will be applicable to all detectors running in Fermilab's Short Baseline Neutrino program (SBN), and to any future liquid argon time projection chamber (LArTPC) experiment with beam energies $\sim 1$ GeV. The algorithms are publicly available on a GITHUB repository\cite{githubcode}. This reconstruction offers a complementary and independent alternative to the Pandora reconstruction package currently in use in LArTPC experiments, and provides similar reconstruction performance for two-track events.
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Semantic Segmentation with a Sparse Convolutional Neural Network for Event Reconstruction in MicroBooNE
TL;DR: SparseSSNet as discussed by the authors is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's appearance oscillation analyses.
A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
P. Abratenko,M. Alrashed,R. An,J. Anthony,J. Asaadi,Adi Ashkenazi,S. Balasubramanian,B. Baller,C. Barnes,G.D. Barr,V. Basque,L. Bathe-Peters,O. Benevides Rodrigues,S. Berkman,A. Bhanderi,A. Bhat,M. Bishai,Andrew Blake,T. A. Bolton,L. Camilleri,D. Caratelli,I. Caro Terrazas,R. Castillo Fernandez,F. Cavanna,Giuseppe Benedetto Cerati,Yi Chen,E. Church,D. Cianci,Jan Conrad,M. E. Convery,L. Cooper-Troendle,J. I. Crespo-Anadón,M. Del Tutto,D. Devitt,R. Diurba,Laura Dominé,R. Dorrill,K. E. Duffy,S. A. Dytman,B. Eberly,Antonio Ereditato,L. Escudero Sanchez,John Evans,G. A. Fiorentini Aguirre,R. S. Fitzpatrick,B. T. Fleming,N. Foppiani,D. Franco,A. P. Furmanski,D. Garcia-Gamez,Steven Gardiner,G. Ge,S. Gollapinni,O. Goodwin,E. Gramellini,Paul J. Green,H. Greenlee,W. Q. Gu,R. Guenette,P. Guzowski,E. D. Hall,P. M. Hamilton,Or Hen,G. A. Horton-Smith,A. Hourlier,E.-C. Huang,R. Itay,C. W. James,J. Jan de Vries,Xiaolu Ji,L. Jiang,J. H. Jo,R. A. Johnson,Y.-J. Jwa,N. Kamp,G. Karagiorgi,W. Ketchum,B. Kirby,M. H. Kirby,T. Kobilarcik,I. Kreslo,R. LaZur,I. Lepetic,K. Li,Yifan Li,B. R. Littlejohn,D. Lorca,W. C. Louis,X. Luo,A. Marchionni,S. Marcocci,C. Mariani,D. Marsden,Jennifer L. Marshall,J. Martin-Albo,D. A. Martinez Caicedo,K. Mason,A. Mastbaum,N. McConkey,V. Meddage,T. Mettler,Kate C. Miller,J. C. Mills,K. P. Mistry,A. Mogan,T. A. Mohayai,J. Moon,Margaret M. Mooney,A. F. Moor,C. D. Moore,J. Mousseau,Michael T. Murphy,D. Naples,A. Navrer-Agasson,R. K. Neely,P. Nienaber,J. A. Nowak,O. Palamara,V. Paolone,Afroditi Papadopoulou,V. Papavassiliou,S. F. Pate,A. Paudel,Z. Pavlovic,E. Piasetzky,I. Ponce-Pinto,D. Porzio,Sebastien Prince,Xin Qian,J. L. Raaf,Veljko Radeka,A. Rafique,M. Reggiani-Guzzo,L. Ren,L.S. Rochester,J. Rodriguez Rondon,H. E. Rogers,M. Rosenberg,M. Ross-Lonergan,B. Russell,G. Scanavini,D.W. Schmitz,A. Schukraft,M. H. Shaevitz,R. Sharankova,J. R. Sinclair,A. M. Smith,E.L. Snider,M. Soderberg,S. Söldner-Rembold,S.R. Soleti,Panagiotis Spentzouris,J. Spitz,M. Stancari,J. St. John,Thomas Strauss,K. Sutton,S. Sword-Fehlberg,A. M. Szelc,N. Tagg,William Tang,Kazuhiro Terao,C. Thorpe,M. Toups,Y. T. Tsai,Serhan Tufanli,M. A. Uchida,T. L. Usher,W. Van De Pontseele,B. Viren,M. Weber,H. Y. Wei,Z. Williams,S. Wolbers,T. Wongjirad,M. Wospakrik,Wei Wu,T. Yang,G. Yarbrough,L.E. Yates,G. P. Zeller,J. Zennamo,C. Zhang +182 more
TL;DR: In this paper, a convolutional neural network (CNN) for multiple object classification was proposed. And the multiple particle identification (MPID) network, a CNN-based approach for multiple particle detection, is presented.
41
Measurement of Neutral Current Elastic Cross Section in MicroBooNE
L. Ren
- 31 Mar 2022
TL;DR: In this article , the flux-averaged neutral-current elastic differential cross sections for neutrinos scattering on argon were measured as a function of Q 2 , proton momentum and proton angle with respect to the direction of the neutrino beam.
Measurement of Neutral Current Elastic Cross Section in MicroBooNE
TL;DR: In this paper , the flux-averaged neutral-current elastic differential cross sections for neutrinos scattering on argon were measured as a function of Q 2 , proton momentum and proton angle with respect to the direction of the neutrino beam.
A hybrid 3D/2D field response calculation for liquid argon detectors with PCB based anode plane
S. Martynenko,F. Pietropaolo,B. Viren,X. Qian,H. S. Chen,S. Gao,W. Q. Gu,J. H. Jo,S. H. Kettell,Y. Li,H. Liu,N. Nayak,B.L. Yu,H. Yu,C. Zhang,Utku Kose,Filippo Resnati,Serhan Tufanli,Fatma Boran,Furkan Dolek +19 more
TL;DR: In this article , the authors present a new software package pochoir that calculates LArTPC field response for these new strip-based anode designs by combining 3D calculations in the volume near the electrodes with 2D far-field solutions to achieve fast and precise field response computation.
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Design and Construction of the MicroBooNE Detector
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TL;DR: MicroBooNE as discussed by the authors is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics.
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