Event triggering and deep learning for particle identification in KM3NeT
2016 - 2017 Neutrino astronomy experiments like KM3NeT allow to survey the Universe leveraging the properties of neutrinos of being electrically neutral and weakly interacting particles, making them a suitable messenger. Observing neutrino emission in association with electromagnetic radiation allow...
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ftunivsalerno:oai:elea.unisa.it:10556/3085 2023-05-15T17:53:59+02:00 Event triggering and deep learning for particle identification in KM3NeT De Sio, Chiara Scarpa, Roberto Bozza, Cristiano 2018-04-11 application/pdf http://hdl.handle.net/10556/3085 https://doi.org/10.14273/unisa-1369 en eng Universita degli studi di Salerno http://hdl.handle.net/10556/3085 http://dx.doi.org/10.14273/unisa-1369 Fisica "E. R. Caianiello" KM3Net Deep learning FIS/04 FISICA NUCLEARE E SUBNUCLEARE Doctoral Thesis 2018 ftunivsalerno https://doi.org/10.14273/unisa-1369 2020-11-27T14:16:13Z 2016 - 2017 Neutrino astronomy experiments like KM3NeT allow to survey the Universe leveraging the properties of neutrinos of being electrically neutral and weakly interacting particles, making them a suitable messenger. Observing neutrino emission in association with electromagnetic radiation allows evaluating models for the acceleration of particles occurring in high energy sources such as Supernovae or Active Galactic Nuclei. This is the main goal of the ARCA project in KM3NeT. In addition, KM3NeT has a program for lower energy neutrinos called ORCA, aimed at distinguishing between the scenarios of “normal hierarchy” and “inverted hierarchy” for neutrino mass eigenstates. The KM3NeT Collaboration is currently building a network of three Cherenkov telescopes in the Mediterranean sea, in deep water off the coasts of Capopassero, Italy; Toulon, France, and Pylos, Greece. The water overburden shields the detectors from down-going charged particles produced by the interactions of cosmic rays in the atmosphere, while up-going neutrinos that cross the Earth are the target of the observation. Cosmic rays are a background to the KM3NeT signal, usually discarded by directional information. Nevertheless, they provide a reliable reference to calibrate the detector and work out its effective operating parameters, namely direction and energy of the incoming particles. Estimation of tracking capabilities is directly connected to the evaluation of the ability of the experiment to detect astrophysical point-like sources, i.e. its discovery potential. Being able to distinguish among the three neutrino flavours, or between neutrinos and muons, as well as estimating the neutrino direction and energy, are the main goals of such experiments. Trigger and reconstruction algorithms are designed to separate the signal from background and to provide an estimation for the above mentioned quantities, respectively. [edited by author] XVI n.s. Doctoral or Postdoctoral Thesis Orca Universita' degli studi di Salerno: elea (Electronic Archive for PhD Thesis) |
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Universita' degli studi di Salerno: elea (Electronic Archive for PhD Thesis) |
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KM3Net Deep learning FIS/04 FISICA NUCLEARE E SUBNUCLEARE |
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KM3Net Deep learning FIS/04 FISICA NUCLEARE E SUBNUCLEARE De Sio, Chiara Event triggering and deep learning for particle identification in KM3NeT |
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KM3Net Deep learning FIS/04 FISICA NUCLEARE E SUBNUCLEARE |
description |
2016 - 2017 Neutrino astronomy experiments like KM3NeT allow to survey the Universe leveraging the properties of neutrinos of being electrically neutral and weakly interacting particles, making them a suitable messenger. Observing neutrino emission in association with electromagnetic radiation allows evaluating models for the acceleration of particles occurring in high energy sources such as Supernovae or Active Galactic Nuclei. This is the main goal of the ARCA project in KM3NeT. In addition, KM3NeT has a program for lower energy neutrinos called ORCA, aimed at distinguishing between the scenarios of “normal hierarchy” and “inverted hierarchy” for neutrino mass eigenstates. The KM3NeT Collaboration is currently building a network of three Cherenkov telescopes in the Mediterranean sea, in deep water off the coasts of Capopassero, Italy; Toulon, France, and Pylos, Greece. The water overburden shields the detectors from down-going charged particles produced by the interactions of cosmic rays in the atmosphere, while up-going neutrinos that cross the Earth are the target of the observation. Cosmic rays are a background to the KM3NeT signal, usually discarded by directional information. Nevertheless, they provide a reliable reference to calibrate the detector and work out its effective operating parameters, namely direction and energy of the incoming particles. Estimation of tracking capabilities is directly connected to the evaluation of the ability of the experiment to detect astrophysical point-like sources, i.e. its discovery potential. Being able to distinguish among the three neutrino flavours, or between neutrinos and muons, as well as estimating the neutrino direction and energy, are the main goals of such experiments. Trigger and reconstruction algorithms are designed to separate the signal from background and to provide an estimation for the above mentioned quantities, respectively. [edited by author] XVI n.s. |
author2 |
Scarpa, Roberto Bozza, Cristiano |
format |
Doctoral or Postdoctoral Thesis |
author |
De Sio, Chiara |
author_facet |
De Sio, Chiara |
author_sort |
De Sio, Chiara |
title |
Event triggering and deep learning for particle identification in KM3NeT |
title_short |
Event triggering and deep learning for particle identification in KM3NeT |
title_full |
Event triggering and deep learning for particle identification in KM3NeT |
title_fullStr |
Event triggering and deep learning for particle identification in KM3NeT |
title_full_unstemmed |
Event triggering and deep learning for particle identification in KM3NeT |
title_sort |
event triggering and deep learning for particle identification in km3net |
publisher |
Universita degli studi di Salerno |
publishDate |
2018 |
url |
http://hdl.handle.net/10556/3085 https://doi.org/10.14273/unisa-1369 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
http://hdl.handle.net/10556/3085 http://dx.doi.org/10.14273/unisa-1369 Fisica "E. R. Caianiello" |
op_doi |
https://doi.org/10.14273/unisa-1369 |
_version_ |
1766161696073711616 |