A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory
Published: July 22, 2021 Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational...
Published in: | Journal of Instrumentation |
---|---|
Main Authors: | , , , , , , , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
IOP Publishing
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/2440/135213 https://doi.org/10.1088/1748-0221/16/07/P07041 |
id |
ftunivadelaidedl:oai:digital.library.adelaide.edu.au:2440/135213 |
---|---|
record_format |
openpolar |
spelling |
ftunivadelaidedl:oai:digital.library.adelaide.edu.au:2440/135213 2023-12-17T10:50:10+01:00 A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory Abbasi, R. Ackermann, M. Adams, J. Aguilar, J.A. Ahlers, M. Ahrens, M. Alispach, C. Alves, A.A. Amin, N.M. An, R. Andeen, K. Anderson, T. Ansseau, I. Anton, G. Argüelles, C. Axani, S. Bai, X. Balagopal, A.V. Barbano, A. Barwick, S.W. 2021 https://hdl.handle.net/2440/135213 https://doi.org/10.1088/1748-0221/16/07/P07041 en eng IOP Publishing Journal of Instrumentation, 2021; 16(7):P07041-1-P07041-39 1748-0221 https://hdl.handle.net/2440/135213 doi:10.1088/1748-0221/16/07/P07041 © 2021 IOP Publishing Ltd and Sissa Medialab http://dx.doi.org/10.1088/1748-0221/16/07/p07041 Data analysis Neutrino detectors Pattern recognition cluster finding calibration and fitting methods Journal article 2021 ftunivadelaidedl https://doi.org/10.1088/1748-0221/16/07/P0704110.1088/1748-0221/16/07/p07041 2023-11-20T23:26:52Z Published: July 22, 2021 Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction method based on convolutional architectures and hexagonally shaped kernels is presented. The presented method is robust towards systematic uncertainties in the simulation and has been tested on experimental data. In comparison to standard reconstruction methods in IceCube, it can improve upon the reconstruction accuracy, while reducing the time necessary to run the reconstruction by two to three orders of magnitude. R. Abbasi . G.C. Hill . A. Kyriacou . A. Wallace . B.J. Whelan . et al. (The IceCube collaboration) Article in Journal/Newspaper South pole The University of Adelaide: Digital Library South Pole Journal of Instrumentation 16 07 P07041 |
institution |
Open Polar |
collection |
The University of Adelaide: Digital Library |
op_collection_id |
ftunivadelaidedl |
language |
English |
topic |
Data analysis Neutrino detectors Pattern recognition cluster finding calibration and fitting methods |
spellingShingle |
Data analysis Neutrino detectors Pattern recognition cluster finding calibration and fitting methods Abbasi, R. Ackermann, M. Adams, J. Aguilar, J.A. Ahlers, M. Ahrens, M. Alispach, C. Alves, A.A. Amin, N.M. An, R. Andeen, K. Anderson, T. Ansseau, I. Anton, G. Argüelles, C. Axani, S. Bai, X. Balagopal, A.V. Barbano, A. Barwick, S.W. A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory |
topic_facet |
Data analysis Neutrino detectors Pattern recognition cluster finding calibration and fitting methods |
description |
Published: July 22, 2021 Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction method based on convolutional architectures and hexagonally shaped kernels is presented. The presented method is robust towards systematic uncertainties in the simulation and has been tested on experimental data. In comparison to standard reconstruction methods in IceCube, it can improve upon the reconstruction accuracy, while reducing the time necessary to run the reconstruction by two to three orders of magnitude. R. Abbasi . G.C. Hill . A. Kyriacou . A. Wallace . B.J. Whelan . et al. (The IceCube collaboration) |
format |
Article in Journal/Newspaper |
author |
Abbasi, R. Ackermann, M. Adams, J. Aguilar, J.A. Ahlers, M. Ahrens, M. Alispach, C. Alves, A.A. Amin, N.M. An, R. Andeen, K. Anderson, T. Ansseau, I. Anton, G. Argüelles, C. Axani, S. Bai, X. Balagopal, A.V. Barbano, A. Barwick, S.W. |
author_facet |
Abbasi, R. Ackermann, M. Adams, J. Aguilar, J.A. Ahlers, M. Ahrens, M. Alispach, C. Alves, A.A. Amin, N.M. An, R. Andeen, K. Anderson, T. Ansseau, I. Anton, G. Argüelles, C. Axani, S. Bai, X. Balagopal, A.V. Barbano, A. Barwick, S.W. |
author_sort |
Abbasi, R. |
title |
A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory |
title_short |
A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory |
title_full |
A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory |
title_fullStr |
A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory |
title_full_unstemmed |
A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory |
title_sort |
convolutional neural network based cascade reconstruction for the icecube neutrino observatory |
publisher |
IOP Publishing |
publishDate |
2021 |
url |
https://hdl.handle.net/2440/135213 https://doi.org/10.1088/1748-0221/16/07/P07041 |
geographic |
South Pole |
geographic_facet |
South Pole |
genre |
South pole |
genre_facet |
South pole |
op_source |
http://dx.doi.org/10.1088/1748-0221/16/07/p07041 |
op_relation |
Journal of Instrumentation, 2021; 16(7):P07041-1-P07041-39 1748-0221 https://hdl.handle.net/2440/135213 doi:10.1088/1748-0221/16/07/P07041 |
op_rights |
© 2021 IOP Publishing Ltd and Sissa Medialab |
op_doi |
https://doi.org/10.1088/1748-0221/16/07/P0704110.1088/1748-0221/16/07/p07041 |
container_title |
Journal of Instrumentation |
container_volume |
16 |
container_issue |
07 |
container_start_page |
P07041 |
_version_ |
1785574862578778112 |