Skip to content
DraconiticResearch
Search Sources API
194,220 studies31 sourcesUpdated 8 min ago
Peer-reviewedCC BY 4.0The Astrophysical Journal

A Statistical Study of Ion-scale Waves at 1 au Using Machine Learning

  • Samuel Fordin1,2
  • Michael A. Shay2
  • Lynn B. Wilson1
  • Jaye L. Verniero1
  • Subash Adhikari2
  • Manuel Andres Herrera Arias3
  • Bilal Khan2
  • Daniel O’Donnell2
  • Barbara Thompson1
  1. 1NASA Goddard Space Flight Center
  2. 2University of Delaware
  3. 3National University of Colombia
Open at sourcePDFDOICite

Abstract

Circularly polarized waves at ion scales are thought to play vital roles in the heating and acceleration of coronal plasma, though the extent of their roles throughout the heliosphere remains an open question. In this work, a 1D convolutional neural network trained to identify waves in magnetic field time-series data is used to classify the Wind spacecraft data set at 1 au for the yr 2005–2024. Approximately 1.8 million intervals out of about one hundred million intervals are found to contain circularly polarized wave signatures with amplitudes >0.1 nT and spacecraft-frame frequencies >0.16 Hz. Wave intervals are observed most often in both the fast, hot wind and in slow Alfvénic wind. The number of wave intervals per day fluctuates at frequencies that closely follow the harmonics of solar rotation, implying a connection between wave occurrence and large-scale solar wind features. Comparison of the measured temperature anisotropy and proton plasma beta in wave intervals to kinetic instability criteria shows a correlation between wave occurrence and the proton cyclotron and mirror instabilities. However, local generation mechanisms associated with core proton temperature anisotropy instability only account for ≈30% of the identified waves.

Related

  1. Single Frequency CMB Foreground Removal with Inter-scale Machine Learning
    Helen Shao, Fiona McCarthy, Blake D. Sherwin +2
    arXivPreprintCC BY 4.0Full textCosmologyarXiv
  2. Singlet Extended Mirror Standard Model World as Dark Matter and Gravitational Waves Imprints of a High Scale Mirror Phase Transitions
    Asterios T. Papadopoulos, V. K. Oikonomou
    arXivPreprintarXiv licenceFull textCosmologyGravitation & general relativityarXiv
  3. Ion-Scale Waves Regulated by Plasma Beta and Cross Helicity: Observational Evidence for the Helicity Barrier in Imbalanced Solar Wind Turbulence
    G. Q. Zhao, R. Meyrand, H. Q. Feng +3
    arXivPreprintCC BY-NC-ND 4.0Full textSolar & stellar physicsarXiv
  4. Thermal Metastable Strings in One-Scale Models and Gravitational Waves
    Arturo de Giorgi, James Ingoldby, Valentin V. Khoze +1
    arXivPreprintCC BY 4.0Full textCosmologyHigh-energy & particle physicsarXiv
  5. Large-scale Vortices in the Solar Photosphere: Statistical Properties and the Reliability of Local Correlation Tracking
    Mashael Aldhafeeri, Suzana S. A. Silva, Istvan Ballai +4
    The Astrophysical JournalPeer-reviewedCC BY 4.0Full textSolar & stellar physicsCrossref

Cite

BibTeXRIS
BibTeX
@article{fordin2026statistical,
  title = {{A Statistical Study of Ion-scale Waves at 1 au Using Machine Learning}},
  author = {Samuel Fordin and Michael A. Shay and Lynn B. Wilson and Jaye L. Verniero and Subash Adhikari and Manuel Andres Herrera Arias and Bilal Khan and Daniel O’Donnell and Barbara Thompson},
  journal = {The Astrophysical Journal},
  year = {2026},
  month = oct,
  doi = {10.3847/1538-4357/aea41b},
  url = {https://iopscience.iop.org/article/10.3847/1538-4357/aea41b},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A Statistical Study of Ion-scale Waves at 1 au Using Machine Learning
AU  - Samuel Fordin
AU  - Michael A. Shay
AU  - Lynn B. Wilson
AU  - Jaye L. Verniero
AU  - Subash Adhikari
AU  - Manuel Andres Herrera Arias
AU  - Bilal Khan
AU  - Daniel O’Donnell
AU  - Barbara Thompson
PY  - 2026
DA  - 2026/10/16
JO  - The Astrophysical Journal
DO  - 10.3847/1538-4357/aea41b
UR  - https://iopscience.iop.org/article/10.3847/1538-4357/aea41b
AB  - Circularly polarized waves at ion scales are thought to play vital roles in the heating and acceleration of coronal plasma, though the extent of their roles throughout the heliosphere remains an open question. In this work, a 1D convolutional neural network trained to identify waves in magnetic field time-series data is used to classify the Wind spacecraft data set at 1 au for the yr 2005–2024. Approximately 1.8 million intervals out of about one hundred million intervals are found to contain circularly polarized wave signatures with amplitudes >0.1 nT and spacecraft-frame frequencies >0.16 Hz. Wave intervals are observed most often in both the fast, hot wind and in slow Alfvénic wind. The number of wave intervals per day fluctuates at frequencies that closely follow the harmonics of solar rotation, implying a connection between wave occurrence and large-scale solar wind features. Comparison of the measured temperature anisotropy and proton plasma beta in wave intervals to kinetic instability criteria shows a correlation between wave occurrence and the proton cyclotron and mirror instabilities. However, local generation mechanisms associated with core proton temperature anisotropy instability only account for ≈30% of the identified waves.
SN  - 0004-637X
SN  - 1538-4357
ER  -