Air shower reconstruction using a Graph Neural Network for the IceAct telescopes
The IceAct telescopes are prototype Imaging Air Cherenkov telescopes(IACTs) situated at the IceCube Neutrino Observatory at the geographic South Pole. The telescopes camera consist of 61 silicon photomultipliers (SiPMs) with a hexagonal light guide glued to each SiPM. The IceAct telescopes measure t...
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ftzenodo:oai:zenodo.org:6354743 2024-09-15T18:36:46+00:00 Air shower reconstruction using a Graph Neural Network for the IceAct telescopes Larissa Paul Thomas Bretz John Hewitt Adrian Zink for the IceCube Collaboration 2022-02-01 https://doi.org/10.5281/zenodo.6354743 eng eng Zenodo https://zenodo.org/communities/ml-airshowers-bartol2022 https://doi.org/10.5281/zenodo.6354742 https://doi.org/10.5281/zenodo.6354743 oai:zenodo.org:6354743 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Workshop on Machine learning for Cosmic-Ray Air Showers, Newark, Delaware, USA + Zoom (hybrid workshop), 31 Jan - 03 Feb 2022 info:eu-repo/semantics/lecture 2022 ftzenodo https://doi.org/10.5281/zenodo.635474310.5281/zenodo.6354742 2024-07-26T21:43:24Z The IceAct telescopes are prototype Imaging Air Cherenkov telescopes(IACTs) situated at the IceCube Neutrino Observatory at the geographic South Pole. The telescopes camera consist of 61 silicon photomultipliers (SiPMs) with a hexagonal light guide glued to each SiPM. The IceAct telescopes measure the electromagnetic air shower component of cosmic rays in the atmosphere, which iscomplementary to the muonic componentmeasuredby theIceCubein-ice detector and the particle footprintmeasuredat the surface by IceTop.The shape of the events andthenumber of SiPMs hit per event within the IceAct telescopes, and the possibility of combining information from different detector components, makes the IceAct data a perfect candidate for a reconstruction of particle type and energyusing a graph neural network (gnn).In contrast to other neural networks, gnns do not need a fixed structure between the nodes, the number nodes can differ between events and the connection between the nodes can be defined individually for each pair of nodes. A Monte Carlo study for a first gnn reconstruction of air shower events with the IceAct telescopes will be presented. Lecture South pole Zenodo |
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description |
The IceAct telescopes are prototype Imaging Air Cherenkov telescopes(IACTs) situated at the IceCube Neutrino Observatory at the geographic South Pole. The telescopes camera consist of 61 silicon photomultipliers (SiPMs) with a hexagonal light guide glued to each SiPM. The IceAct telescopes measure the electromagnetic air shower component of cosmic rays in the atmosphere, which iscomplementary to the muonic componentmeasuredby theIceCubein-ice detector and the particle footprintmeasuredat the surface by IceTop.The shape of the events andthenumber of SiPMs hit per event within the IceAct telescopes, and the possibility of combining information from different detector components, makes the IceAct data a perfect candidate for a reconstruction of particle type and energyusing a graph neural network (gnn).In contrast to other neural networks, gnns do not need a fixed structure between the nodes, the number nodes can differ between events and the connection between the nodes can be defined individually for each pair of nodes. A Monte Carlo study for a first gnn reconstruction of air shower events with the IceAct telescopes will be presented. |
format |
Lecture |
author |
Larissa Paul Thomas Bretz John Hewitt Adrian Zink for the IceCube Collaboration |
spellingShingle |
Larissa Paul Thomas Bretz John Hewitt Adrian Zink for the IceCube Collaboration Air shower reconstruction using a Graph Neural Network for the IceAct telescopes |
author_facet |
Larissa Paul Thomas Bretz John Hewitt Adrian Zink for the IceCube Collaboration |
author_sort |
Larissa Paul |
title |
Air shower reconstruction using a Graph Neural Network for the IceAct telescopes |
title_short |
Air shower reconstruction using a Graph Neural Network for the IceAct telescopes |
title_full |
Air shower reconstruction using a Graph Neural Network for the IceAct telescopes |
title_fullStr |
Air shower reconstruction using a Graph Neural Network for the IceAct telescopes |
title_full_unstemmed |
Air shower reconstruction using a Graph Neural Network for the IceAct telescopes |
title_sort |
air shower reconstruction using a graph neural network for the iceact telescopes |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://doi.org/10.5281/zenodo.6354743 |
genre |
South pole |
genre_facet |
South pole |
op_source |
Workshop on Machine learning for Cosmic-Ray Air Showers, Newark, Delaware, USA + Zoom (hybrid workshop), 31 Jan - 03 Feb 2022 |
op_relation |
https://zenodo.org/communities/ml-airshowers-bartol2022 https://doi.org/10.5281/zenodo.6354742 https://doi.org/10.5281/zenodo.6354743 oai:zenodo.org:6354743 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.635474310.5281/zenodo.6354742 |
_version_ |
1810480479162335232 |