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, A. Ashkenazi, S. Balasubramanian, B. Baller, C. Barnes, G. Barr, V. Basque, L. Bathe-Peters, O. Benevides Rodrigues, S. Berkman, A. Bhanderi, A. Bhat, M. Bishai, A. Blake, T. Bolton, L. Camilleri, D. Caratelli, I. Caro Terrazas, R. Castillo Fernández, F. Cavanna, G. B. Cerati, Y. Chen, E. Church, D. Cianci, J. M. Conrad, M. Convery, L. Cooper-Troendle, J. I. Crespo-Anadón, M. Del Tutto, Ann Devitt, Z. Djurcic, L. Domine, R. Dorrill, K. Duffy, S. Dytman, B. Eberly, A. Ereditato, L. Escudero Sanchez, J. J. Evans, G. A. Fiorentini Aguirre, R. S. Fitzpatrick, B. T. Fleming, N. Foppiani, D. Franco, A. P. Furmanski, D. García-Gámez, S. Gardiner, G. Ge, S. Gollapinni, O. Goodwin, E. Gramellini, P. Green, H. Greenlee, Wei Gu, R. Guénette, P. Guzowski, E. Hall, P. Hamilton, O. Hen, G. A. Horton-Smith, A. Hourlier, E.-C. Huang, R. Itay, C. James, J. Jan de Vries, X. Ji, L. Jiang, J. H. Jo, R. A. Johnson, Y.-J. Jwa, N. Kamp, G. Karagiorgi, W. Ketchum, B. Kirby, M. Kirby, T. Kobilarcik, I. Kreslo, R. LaZur, I. Lepetic, K. Li, Y. Li, B. R. Littlejohn, D. Lorca, W. C. Louis, X. Luo, A. Marchionni, S. Marcocci, C. Mariani, D. Marsden, J. Marshall, J. Martín-Albo, D. A. Martínez Caicedo, K. Mason, A. Mastbaum, N. McConkey, V. Meddage
Abstract
We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an interaction includes an ${e}^{\ensuremath{-}}$, $\ensuremath{\gamma}$, ${\ensuremath{\mu}}^{\ensuremath{-}}$, ${\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}$, and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE [Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017)]. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep-learning-based ${\ensuremath{\nu}}_{e}$ search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.