Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation
Authors: Abratenko P. , An R. , Anthony J. , Arellano L. , Asaadi J. , Ashkenazi A. , Balasubramanian S. , Baller B. , Barnes C. , Barr G. , Basque V. , Bathe-Peters L. , Benevides Rodrigues O. , Berkman S. , Bhanderi A. , Bhat A. , Bishai M. , Blake A. , Bolton T. , Book J.Y. , Camilleri L. , Caratelli D. , Caro Terrazas I. , Castillo Fernandez R. , Cavanna F. , Cerati G. , Chen Y. , Cianci D. , Conrad J.M. , Convery M. , Cooper-Troendle L. , Crespo-Anadón J.I. , Del Tutto M. , Dennis S.R. , Detje P. , Devitt A. , Diurba R. , Dorrill R. , Duffy K. , Dytman S. , Eberly B. , Ereditato A. , Evans J.J. , Fine R. , Fiorentini Aguirre G.A. , Fitzpatrick R.S. , Fleming B.T. , Foppiani N. , Franco D. , Furmanski A.P. , Garcia-Gamez D. , Gardiner S. , Ge G. , Gollapinni S. , Goodwin O. , Gramellini E. , Green P. , Greenlee H. , Gu W. , Guenette R. , Guzowski P. , Hagaman L. , Hen O. , Hilgenberg C. , Horton-Smith G.A. , Hourlier A. , Itay R. , James C. , Ji X. , Jiang L. , Jo J.H. , Johnson R.A. , Jwa Y.-J. , Kalra D. , Kamp N. , Kaneshige N. , Karagiorgi G. , Ketchum W. , Kirby M. , Kobilarcik T. , Kreslo I. , LaZur R. , Lepetic I. , Li K. , Li Y. , Lin K. , Littlejohn B.R. , Louis W.C. , Luo X. , Manivannan K. , Mariani C. , Marsden D. , Marshall J. , Martinez Caicedo D.A. , Mason K. , Mastbaum A. , McConkey N. , Meddage V. , Mettler T. , Miller K. , Mills J. , Mistry K. , Mogan A. , Mohayai T. , Moon J. , Mooney M. , Moor A.F. , Moore C.D. , Mora Lepin L. , Mousseau J. , Murphy M. , Naples D. , Navrer-Agasson A. , Nebot-Guinot M. , Neely R.K. , Newmark D.A. , Nowak J. , Nunes M. , Palamara O. , Paolone V. , Papadopoulou A. , Papavassiliou V. , Pate S.F. , Patel N. , Paudel A. , Pavlovic Z. , Piasetzky E. , Ponce-Pinto I.D. , Prince S. , Qian X. , Raaf J.L. , Radeka V. , Rafique A. , Reggiani-Guzzo M. , Ren L. , Rice L.C.J. , Rochester L. , Rodriguez Rondon J. , Rosenberg M. , Ross-Lonergan M. , Scanavini G. , Schmitz D.W. , Schukraft A. , Seligman W. , Shaevitz M.H. , Sharankova R. , Shi J. , Sinclair J. , Smith A. , Snider E.L. , Soderberg M. , Söldner-Rembold S. , Spentzouris P. , Spitz J. , Stancari M. , St. John J. , Strauss T. , Sutton K. , Sword-Fehlberg S. , Szelc A.M. , Tagg N. , Tang W. , Terao K. , Thorpe C. , Totani D. , Toups M. , Tsai Y.-T. , Uchida M.A. , Usher T. , Van De Pontseele W. , Viren B. , Weber M. , Wei H. , Williams Z. , Wolbers S. , Wongjirad T. , Wospakrik M. , Wresilo K. , Wright N. , Wu W. , Yandel E. , Yang T. , Yarbrough G. , Yates L.E. , Yu H.W. , Zeller G.P. , Zennamo J. , Zhang C. 
Published: January 2022

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.