Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory

The evaluation of mass composition of cosmic rays in the knee region ($\sim 3$ PeV) is critical to understanding the transition in the origin of cosmic rays from galactic to extragalactic sources. The IceCube Neutrino Observatory at the South Pole is a multi-component detector consisting of the surf...

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Bibliographic Details
Main Author: Plum, Matthias
Format: Article in Journal/Newspaper
Language:unknown
Published: arXiv 2019
Subjects:
High Energy Astrophysical Phenomena astro-ph.HE
FOS Physical sciences
South Pole
South pole
Online Access:https://dx.doi.org/10.48550/arxiv.1908.06433
https://arxiv.org/abs/1908.06433
id ftdatacite:10.48550/arxiv.1908.06433
record_format openpolar
spelling ftdatacite:10.48550/arxiv.1908.06433 2023-05-15T18:22:20+02:00 Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory Plum, Matthias 2019 https://dx.doi.org/10.48550/arxiv.1908.06433 https://arxiv.org/abs/1908.06433 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ High Energy Astrophysical Phenomena astro-ph.HE FOS Physical sciences Article CreativeWork article Preprint 2019 ftdatacite https://doi.org/10.48550/arxiv.1908.06433 2022-03-10T16:27:39Z The evaluation of mass composition of cosmic rays in the knee region ($\sim 3$ PeV) is critical to understanding the transition in the origin of cosmic rays from galactic to extragalactic sources. The IceCube Neutrino Observatory at the South Pole is a multi-component detector consisting of the surface IceTop array and the deep in-ice IceCube detector. By applying modern machine-learning techniques to cosmic-ray air showers reconstructed coincidentally in both detector components of IceCube observatory, the energy and the mass of primary cosmic rays in this transition region can be measured. In this contribution, we will discuss the reconstruction performance and composition sensitivity of IceCube observables presently under development. : Presented at the 36th International Cosmic Ray Conference (ICRC 2019). See arXiv:1907.11699 for all IceCube contributions Article in Journal/Newspaper South pole DataCite Metadata Store (German National Library of Science and Technology) South Pole
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic High Energy Astrophysical Phenomena astro-ph.HE
FOS Physical sciences
spellingShingle High Energy Astrophysical Phenomena astro-ph.HE
FOS Physical sciences
Plum, Matthias
Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory
topic_facet High Energy Astrophysical Phenomena astro-ph.HE
FOS Physical sciences
description The evaluation of mass composition of cosmic rays in the knee region ($\sim 3$ PeV) is critical to understanding the transition in the origin of cosmic rays from galactic to extragalactic sources. The IceCube Neutrino Observatory at the South Pole is a multi-component detector consisting of the surface IceTop array and the deep in-ice IceCube detector. By applying modern machine-learning techniques to cosmic-ray air showers reconstructed coincidentally in both detector components of IceCube observatory, the energy and the mass of primary cosmic rays in this transition region can be measured. In this contribution, we will discuss the reconstruction performance and composition sensitivity of IceCube observables presently under development. : Presented at the 36th International Cosmic Ray Conference (ICRC 2019). See arXiv:1907.11699 for all IceCube contributions
format Article in Journal/Newspaper
author Plum, Matthias
author_facet Plum, Matthias
author_sort Plum, Matthias
title Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory
title_short Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory
title_full Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory
title_fullStr Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory
title_full_unstemmed Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory
title_sort cosmic ray composition study using machine learning at the icecube neutrino observatory
publisher arXiv
publishDate 2019
url https://dx.doi.org/10.48550/arxiv.1908.06433
https://arxiv.org/abs/1908.06433
geographic South Pole
geographic_facet South Pole
genre South pole
genre_facet South pole
op_rights arXiv.org perpetual, non-exclusive license
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
op_doi https://doi.org/10.48550/arxiv.1908.06433
_version_ 1766201737721413632

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