Data mining of cosmic-ray anisotropy observed with the Global Muon Detector Network
M. Kozai*,
Y. Hayashi,
C. Kato,
K. Munakata,
Y. Masuda,
K. Iwai,
M. Rockenbach,
A. Dal Lago,
R. R. S. Mendonca,
E. Echer,
J. V. Bageston,
C. R. Braga,
H. K. Al Jassar,
M. M. Sharma,
M. L. Duldig,
J. E. Humble,
A. Kadokura,
R. Kataoka,
S. Miyake,
I. Sabbah,
P.S. Mangeard,
T. Kuwabara and
P. Evenson*: corresponding author
Pre-published on:
August 18, 2023
Published on:
September 27, 2024
Abstract
Ground-based muon detectors are sensitive to anisotropies of cosmic rays at approximately 50 GeV and have been operated with great stability for over 10 years. The anisotropy is controlled by the space environment in various time scales and is an attractive target of data-driven approaches. We report on unsupervised machine-learning of anisotropy data accumulated by the Global Muon Detector Network (GMDN). It is expected to provide a comprehensive and statistical picture of
cosmic-ray anisotropies free from biases such as data selections relying on a visual inspection.
DOI: https://doi.org/10.22323/1.444.1279
How to cite
Metadata are provided both in
article format (very
similar to INSPIRE)
as this helps creating very compact bibliographies which
can be beneficial to authors and readers, and in
proceeding format which
is more detailed and complete.
