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@ARTICLE{Charan:634234,
      author       = {Charan, Abtin Narimani},
      title        = {{P}article identification with the {B}elle {II} calorimeter
                      using machine learning},
      journal      = {Journal of physics / Conference Series},
      volume       = {2438},
      number       = {1},
      issn         = {1742-6588},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {PUBDB-2025-02482, arXiv:2301.11654},
      pages        = {012111 -},
      year         = {2023},
      note         = {5 pages, 6 figures, Proceedings for poster contribution to
                      20th International Workshop on Advanced Computing and
                      Analysis Techniques in Physics Research, 29 November - 3
                      December 2021, It will be published in: IOP Conference
                      Series},
      abstract     = {I present an application of a convolutional neural network
                      (CNN) to separate muons and pions in the Belle II
                      electromagnetic calorimeter (ECL). The ECL is designed to
                      measure the energy deposited by charged and neutral
                      particles. It also provides important contributions to the
                      particle identification (PID) system. Identification of
                      low-momenta muons and pions in the ECL is crucial if they do
                      not reach the outer muon detector. Track-seeded cluster
                      energy images provide the maximal possible information. The
                      shape of the energy depositions for muons and pions in the
                      crystals around an extrapolated track at the entering point
                      of the ECL is used together with crystal positions in θ −
                      ϕ plane and transverse momentum of the track to train a
                      CNN. The CNN exploits the difference between the dispersed
                      energy depositions from pion hadronic interactions and the
                      more localized muon electromagnetic interactions. Using
                      simulation, the performance of the CNN algorithm is compared
                      with other PID methods at Belle II which are based on
                      track-matched clustering information. The results show that
                      the CNN PID method improves muon-pion separation in low
                      momentum.},
      month         = {Nov},
      date          = {2021-11-29},
      organization  = {20th International Workshop on
                       Advanced Computing and Analysis
                       Techniques in Physics Research, Daejeon
                       (South Korea), 29 Nov 2021 - 3 Dec
                       2021},
      keywords     = {electron positron: annihilation (INSPIRE) / electron
                      positron: colliding beams (INSPIRE) / momentum: low
                      (INSPIRE) / muon: detector (INSPIRE) / tracks: transverse
                      momentum (INSPIRE) / calorimeter: electromagnetic (INSPIRE)
                      / muon: particle identification (INSPIRE) / pi: particle
                      identification (INSPIRE) / BELLE (INSPIRE) / crystal
                      (INSPIRE) / electromagnetic interaction (INSPIRE) / cluster
                      (INSPIRE) / machine learning (INSPIRE) / neutral particle
                      (INSPIRE) / particle identification: performance (INSPIRE) /
                      neural network (INSPIRE) / statistical analysis (INSPIRE) /
                      data analysis method (INSPIRE) / numerical calculations:
                      Monte Carlo (INSPIRE) / experimental results (INSPIRE)},
      cin          = {BELLE},
      ddc          = {530},
      cid          = {I:(DE-H253)BELLE-20210408},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611)},
      pid          = {G:(DE-HGF)POF4-611},
      experiment   = {EXP:(DE-H253)BELLE-20150101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)16},
      eprint       = {2301.11654},
      howpublished = {arXiv:2301.11654},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2301.11654;\%\%$},
      doi          = {10.1088/1742-6596/2438/1/012111},
      url          = {https://bib-pubdb1.desy.de/record/634234},
}