Utilizing AI to unveil the nonlinear interplay of convection, drift, and diffusion on galactic cosmic ray modulation in the inner heliosphere

Inceoglu, Fadil; Pacini, Alessandra Abe; Loto’aniu, Paul T. M.

doi:10.1038/s41598-022-25277-0

i

Utilizing AI to unveil the nonlinear interplay of convection, drift, and diffusion on galactic cosmic ray modulation in the inner heliosphere

2022
By Inceoglu, Fadil ; Pacini, Alessandra Abe ; Loto’aniu, Paul T. M.

Details

Journal Title:

Scientific Reports
Personal Author:

Inceoglu, Fadil ; Pacini, Alessandra Abe ; Loto’aniu, Paul T. M.
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; CIRES (Cooperative Institute for Research in Environmental Sciences) ; NCEI (National Centers for Environmental Information)
Description:

Galactic Cosmic Rays (GCRs) are charged particles, originating from galactic and/or extra-galactic Supernova Remnants (SNR), that continuously permeate the Heliosphere. The GCRs are modulated in the heliosphere by convection by solar wind (SW), drift via gradients and curvatures in the Heliospheric Magnetic Field (HMF), diffusion from fluctuations in the HMF, and adiabatic cooling in the expanding SW. An improved understanding of their modulation is imperative as studies on the variations in solar activity levels and solar eruptions in the past rely heavily on the relationship between their modulation and formation of the secondary particles in the Earth’s atmosphere. Here, for the first time, we utilize an AI method, Light Gradient Boosting Machines (LightGBM), to investigate the nonlinear interplay among the modulation processes in different timescales. Our study indicates that the nonlinear interplay among the mechanisms responsible for the GCR modulation in the inner heliosphere are not limited to the scenario of “drift-dominated solar minimum” versus “diffusion-dominated solar maximum”, instead they have dynamic behavior displaying variations in time and in timescales. This study also demonstrates the value of using AI methods to investigate non-linear physical processes in Space Physics in the era of big data.
Keywords:

Multidisciplinary
Source:

Scientific Reports, 12(1)
DOI:

https://doi.org/10.1038/s41598-022-25277-0
ISSN:

2045-2322
Format:

PDF
Publisher:

Springer Science and Business Media LLC
Document Type:

Journal Article
Funding:

Grant no: NA17OAR4320101
License:

https://creativecommons.org/licenses/by/4.0
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

urn:sha256:6af8ec0ba6beccab805107558ce2d39a4deb40417230b4bd9b7893145cef0a77
Download URL:

https://repository.library.noaa.gov/view/noaa/50609/noaa_50609_DS1.pdf
File Type:

[PDF - 1.80 MB ]

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