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@ARTICLE{Rebber:890612,
      author       = {Rebber, H. and Ludhova, L. and Wonsak, B. and Xu, Yu},
      title        = {{P}article identification at {M}e{V} energies in {JUNO}},
      journal      = {Journal of Instrumentation},
      volume       = {16},
      number       = {01},
      issn         = {1748-0221},
      address      = {London},
      publisher    = {Inst. of Physics},
      reportid     = {FZJ-2021-01073},
      pages        = {P01016 - P01016},
      year         = {2021},
      abstract     = {JUNO is a multi-purpose neutrino experiment currently under
                      construction in Jiangmen, China. It is primary aiming to
                      determine the neutrino mass ordering. Moreover, its 20 kt
                      target mass makes it an ideal detector to study neutrinos
                      from various sources, including nuclear reactors, the Earth
                      and its atmosphere, the Sun, and even supernovae. Due to the
                      small cross section of neutrino interactions, the event rate
                      of neutrino experiments is limited. In order to maximize the
                      signal-to-noise ratio, it is extremely important to control
                      the background levels. In this paper we discuss the
                      potential of particle identification in JUNO, its underlying
                      principles and possible areas of application in the
                      experiment. While the presented concepts can be transferred
                      to any large liquid scintillator detector, our methods are
                      evaluated specifically for JUNO and the results are mainly
                      driven by its high optical photon yield of 1,200 photo
                      electrons per MeV of deposited energy. In order to
                      investigate the potential of event discrimination, several
                      event pairings are analysed, i.e. $\alpha/\beta$, $p/β$,
                      $e^+/e^−$, and $e^−/\gamma$. We compare the
                      discrimination performance of advanced analytical techniques
                      based on neural networks and on the topological event
                      reconstruction keeping the standard Gatti filter as a
                      reference. We use the Monte Carlo samples generated in the
                      physically motivated energy intervals. We study the
                      dependence of our cuts on energy, radial position, PMT time
                      resolution, and dark noise. The results show an excellent
                      performance for $α/β$ and $p/β$ with the Gatti method and
                      the neural network. Furthermore, $e^+/e^−$ and $e^−/γ$
                      can partly be distinguished by means of neural network and
                      topological reconstruction on a statistical basis.
                      Especially in the latter case, the topological method proved
                      very successful.},
      cin          = {IKP-2},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IKP-2-20111104},
      pnm          = {612 - Cosmic Matter in the Laboratory (POF4-612)},
      pid          = {G:(DE-HGF)POF4-612},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000663343100023},
      doi          = {10.1088/1748-0221/16/01/P01016},
      url          = {https://juser.fz-juelich.de/record/890612},
}