Local environmental effects on cosmic ray observations at Syowa Station in the Antarctic: PARMA-based snow cover correction for neutrons and machine learning approach for neutrons and muons
Solar modulation of galactic cosmic rays around the solar minimum in 2019–2020 looks different in the secondary neutrons and muons observed at the ground. To compare the solar modulation of primary cosmic rays in detail, we must remove the possible seasonal variations caused by the atmosphere and su...
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ftdoajarticles:oai:doaj.org/article:e1e8eca4aa864fc69a773c49a99e9cd4 2023-05-15T13:44:14+02:00 Local environmental effects on cosmic ray observations at Syowa Station in the Antarctic: PARMA-based snow cover correction for neutrons and machine learning approach for neutrons and muons Kataoka Ryuho Sato Tatsuhiko Kato Chihiro Kadokura Akira Kozai Masayoshi Miyake Shoko Murase Kiyoka Yoshida Lihito Tomikawa Yoshihiro Munakata Kazuoki 2022-01-01T00:00:00Z https://doi.org/10.1051/swsc/2022033 https://doaj.org/article/e1e8eca4aa864fc69a773c49a99e9cd4 EN eng EDP Sciences https://www.swsc-journal.org/articles/swsc/full_html/2022/01/swsc220044/swsc220044.html https://doaj.org/toc/2115-7251 2115-7251 doi:10.1051/swsc/2022033 https://doaj.org/article/e1e8eca4aa864fc69a773c49a99e9cd4 Journal of Space Weather and Space Climate, Vol 12, p 37 (2022) galactic cosmic rays solar modulation ground observation neutron monitor muon detector seasonal variation snow cover machine learning echo state network Meteorology. Climatology QC851-999 article 2022 ftdoajarticles https://doi.org/10.1051/swsc/2022033 2022-12-30T21:13:37Z Solar modulation of galactic cosmic rays around the solar minimum in 2019–2020 looks different in the secondary neutrons and muons observed at the ground. To compare the solar modulation of primary cosmic rays in detail, we must remove the possible seasonal variations caused by the atmosphere and surrounding environment. As such surrounding environment effects, we evaluate the snow cover effect on neutron count rate and the atmospheric temperature effect on muon count rate, both simultaneously observed at Syowa Station in the Antarctic (69.01° S, 39.59° E). A machine learning technique, Echo State Network (ESN), is applied to estimate both effects hidden in the observed time series of the count rate. We show that the ESN with the input of GDAS data (temperature time series at 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, and 20 hPa) at the local position can be useful for both the temperature correction for muons and snow cover correction for neutrons. The corrected muon count rate starts decreasing in late 2019, preceding the corrected neutron count rate which starts decreasing in early 2020, possibly indicating the rigidity-dependent solar modulation in the heliosphere. Article in Journal/Newspaper Antarc* Antarctic Directory of Open Access Journals: DOAJ Articles Antarctic The Antarctic Syowa Station Parma ENVELOPE(57.468,57.468,65.951,65.951) Journal of Space Weather and Space Climate 12 37 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
galactic cosmic rays solar modulation ground observation neutron monitor muon detector seasonal variation snow cover machine learning echo state network Meteorology. Climatology QC851-999 |
spellingShingle |
galactic cosmic rays solar modulation ground observation neutron monitor muon detector seasonal variation snow cover machine learning echo state network Meteorology. Climatology QC851-999 Kataoka Ryuho Sato Tatsuhiko Kato Chihiro Kadokura Akira Kozai Masayoshi Miyake Shoko Murase Kiyoka Yoshida Lihito Tomikawa Yoshihiro Munakata Kazuoki Local environmental effects on cosmic ray observations at Syowa Station in the Antarctic: PARMA-based snow cover correction for neutrons and machine learning approach for neutrons and muons |
topic_facet |
galactic cosmic rays solar modulation ground observation neutron monitor muon detector seasonal variation snow cover machine learning echo state network Meteorology. Climatology QC851-999 |
description |
Solar modulation of galactic cosmic rays around the solar minimum in 2019–2020 looks different in the secondary neutrons and muons observed at the ground. To compare the solar modulation of primary cosmic rays in detail, we must remove the possible seasonal variations caused by the atmosphere and surrounding environment. As such surrounding environment effects, we evaluate the snow cover effect on neutron count rate and the atmospheric temperature effect on muon count rate, both simultaneously observed at Syowa Station in the Antarctic (69.01° S, 39.59° E). A machine learning technique, Echo State Network (ESN), is applied to estimate both effects hidden in the observed time series of the count rate. We show that the ESN with the input of GDAS data (temperature time series at 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, and 20 hPa) at the local position can be useful for both the temperature correction for muons and snow cover correction for neutrons. The corrected muon count rate starts decreasing in late 2019, preceding the corrected neutron count rate which starts decreasing in early 2020, possibly indicating the rigidity-dependent solar modulation in the heliosphere. |
format |
Article in Journal/Newspaper |
author |
Kataoka Ryuho Sato Tatsuhiko Kato Chihiro Kadokura Akira Kozai Masayoshi Miyake Shoko Murase Kiyoka Yoshida Lihito Tomikawa Yoshihiro Munakata Kazuoki |
author_facet |
Kataoka Ryuho Sato Tatsuhiko Kato Chihiro Kadokura Akira Kozai Masayoshi Miyake Shoko Murase Kiyoka Yoshida Lihito Tomikawa Yoshihiro Munakata Kazuoki |
author_sort |
Kataoka Ryuho |
title |
Local environmental effects on cosmic ray observations at Syowa Station in the Antarctic: PARMA-based snow cover correction for neutrons and machine learning approach for neutrons and muons |
title_short |
Local environmental effects on cosmic ray observations at Syowa Station in the Antarctic: PARMA-based snow cover correction for neutrons and machine learning approach for neutrons and muons |
title_full |
Local environmental effects on cosmic ray observations at Syowa Station in the Antarctic: PARMA-based snow cover correction for neutrons and machine learning approach for neutrons and muons |
title_fullStr |
Local environmental effects on cosmic ray observations at Syowa Station in the Antarctic: PARMA-based snow cover correction for neutrons and machine learning approach for neutrons and muons |
title_full_unstemmed |
Local environmental effects on cosmic ray observations at Syowa Station in the Antarctic: PARMA-based snow cover correction for neutrons and machine learning approach for neutrons and muons |
title_sort |
local environmental effects on cosmic ray observations at syowa station in the antarctic: parma-based snow cover correction for neutrons and machine learning approach for neutrons and muons |
publisher |
EDP Sciences |
publishDate |
2022 |
url |
https://doi.org/10.1051/swsc/2022033 https://doaj.org/article/e1e8eca4aa864fc69a773c49a99e9cd4 |
long_lat |
ENVELOPE(57.468,57.468,65.951,65.951) |
geographic |
Antarctic The Antarctic Syowa Station Parma |
geographic_facet |
Antarctic The Antarctic Syowa Station Parma |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_source |
Journal of Space Weather and Space Climate, Vol 12, p 37 (2022) |
op_relation |
https://www.swsc-journal.org/articles/swsc/full_html/2022/01/swsc220044/swsc220044.html https://doaj.org/toc/2115-7251 2115-7251 doi:10.1051/swsc/2022033 https://doaj.org/article/e1e8eca4aa864fc69a773c49a99e9cd4 |
op_doi |
https://doi.org/10.1051/swsc/2022033 |
container_title |
Journal of Space Weather and Space Climate |
container_volume |
12 |
container_start_page |
37 |
_version_ |
1766199266327396352 |