| Issue |
EPJ Web of Conferences
Volume 116, 2016
Very Large Volume Neutrino Telescope (VLVnT-2015)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 4 | |
| Section | Challenges in Simulation and Reconstruction | |
| DOI | https://doi.org/10.1051/epjconf/201611602001 | |
| Published online | 11 April 2016 | |
https://doi.org/10.1051/epjconf/201611602001
Energy reconstruction of high energy muon and neutrino events in KM3NeT
1 N.C.S.R. Demokritos, Patriarchou Grigoriou and Neapoleos 27, Agia Paraskevi, Greece
2 National Technical University of Athens, Heroon Polytechniou 9, Zografou Campus, Greece
a e-mail: liliadrak@inp.demokritos.gr
b e-mail: cmarkou@inp.demokritos.gr
c e-mail: katerina@inp.demokritos.gr
d e-mail: pikounis@inp.demokritos.gr
Published online: 11 April 2016
KM3NeT will be a European deep-sea infrastructure of neutrino telescopes covering a volume of several cubic kilometers in the Mediterranean Sea aiming to search for high energy neutrinos from galactic and extragalactic sources. This analysis focuses on muons coming from neutrino charged-current interactions. In large water Cherenkov detectors the reconstructed muon is used to approximate the neutrino direction and energy, thus providing information on the astrophysical neutrino source. Muon energy estimation is also critical for the differentiation of neutrinos originating from astrophysical sources from neutrinos generated in the atmosphere which constitute the detector background. We describe a method to determine the muon and neutrino energy employing a Neural Network. An energy resolution of approximately 0.27 has been achieved for muons at the TeV range.
© Owned by the authors, published by EDP Sciences, 2016
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