Cosmic rays primary energy estimation using Machine Learning and combined reconstruction

The IceCube Neutrino Observatory at the South Pole is capable of measuring two components of the cosmic rays air shower. The electromagnetic component using a km2 surface array IceTop, and the high-energy muonic component using km3 in-ice array IceCube between 1.5 and 2.5 km below the surface. The c...

Full description

Bibliographic Details
Main Authors: Silverio, Diana Leon, Bai, Xinhua, Plum, Matthias, for the IceCube Collaboration
Format: Lecture
Language:English
Published: Zenodo 2022
Subjects:
South pole
Online Access:https://doi.org/10.5281/zenodo.6525169
id ftzenodo:oai:zenodo.org:6525169
record_format openpolar
spelling ftzenodo:oai:zenodo.org:6525169 2024-09-15T18:36:41+00:00 Cosmic rays primary energy estimation using Machine Learning and combined reconstruction Silverio, Diana Leon Bai, Xinhua Plum, Matthias for the IceCube Collaboration 2022-02-01 https://doi.org/10.5281/zenodo.6525169 eng eng Zenodo https://zenodo.org/communities/ml-airshowers-bartol2022 https://doi.org/10.5281/zenodo.6525168 https://doi.org/10.5281/zenodo.6525169 oai:zenodo.org:6525169 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.652516910.5281/zenodo.6525168 2024-07-26T13:16:10Z The IceCube Neutrino Observatory at the South Pole is capable of measuring two components of the cosmic rays air shower. The electromagnetic component using a km2 surface array IceTop, and the high-energy muonic component using km3 in-ice array IceCube between 1.5 and 2.5 km below the surface. The combination of both arrays in conjunction with a new flexible curvature and new timing fluctuation function provides an opportunity for possible improvements of cosmic rays reconstruction. This work presents a preliminary investigation of possible improvements of cosmic rays primary energy estimation (proton, iron, helium, and oxygen) by using Machine Learning techniques and combined reconstruction. Lecture South pole Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language English
description The IceCube Neutrino Observatory at the South Pole is capable of measuring two components of the cosmic rays air shower. The electromagnetic component using a km2 surface array IceTop, and the high-energy muonic component using km3 in-ice array IceCube between 1.5 and 2.5 km below the surface. The combination of both arrays in conjunction with a new flexible curvature and new timing fluctuation function provides an opportunity for possible improvements of cosmic rays reconstruction. This work presents a preliminary investigation of possible improvements of cosmic rays primary energy estimation (proton, iron, helium, and oxygen) by using Machine Learning techniques and combined reconstruction.
format Lecture
author Silverio, Diana Leon
Bai, Xinhua
Plum, Matthias
for the IceCube Collaboration
spellingShingle Silverio, Diana Leon
Bai, Xinhua
Plum, Matthias
for the IceCube Collaboration
Cosmic rays primary energy estimation using Machine Learning and combined reconstruction
author_facet Silverio, Diana Leon
Bai, Xinhua
Plum, Matthias
for the IceCube Collaboration
author_sort Silverio, Diana Leon
title Cosmic rays primary energy estimation using Machine Learning and combined reconstruction
title_short Cosmic rays primary energy estimation using Machine Learning and combined reconstruction
title_full Cosmic rays primary energy estimation using Machine Learning and combined reconstruction
title_fullStr Cosmic rays primary energy estimation using Machine Learning and combined reconstruction
title_full_unstemmed Cosmic rays primary energy estimation using Machine Learning and combined reconstruction
title_sort cosmic rays primary energy estimation using machine learning and combined reconstruction
publisher Zenodo
publishDate 2022
url https://doi.org/10.5281/zenodo.6525169
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.6525168
https://doi.org/10.5281/zenodo.6525169
oai:zenodo.org:6525169
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.652516910.5281/zenodo.6525168
_version_ 1810480387295543296

Similar Items