Standard

Automated Classification of Plasma Regions Using 3D Particle Energy Distributions. / Olshevsky, Vyacheslav; Khotyaintsev, Yuri V.; Lalti, Ahmad; Divin, Andrey; Delzanno, Gian Luca; Anderzén, Sven; Herman, Pawel; Chien, Steven W.D.; Avanov, Levon; Dimmock, Andrew P.; Markidis, Stefano.

In: journal of geophysical research: Space Physics, Vol. 126, No. 10, e2021JA029620, 10.09.2021.

Research output: Contribution to journal › Article › peer-review

Harvard

Olshevsky, V, Khotyaintsev, YV, Lalti, A, Divin, A, Delzanno, GL, Anderzén, S, Herman, P, Chien, SWD, Avanov, L, Dimmock, AP & Markidis, S 2021, 'Automated Classification of Plasma Regions Using 3D Particle Energy Distributions', journal of geophysical research: Space Physics, vol. 126, no. 10, e2021JA029620. https://doi.org/10.1029/2021JA029620

APA

Olshevsky, V., Khotyaintsev, Y. V., Lalti, A., Divin, A., Delzanno, G. L., Anderzén, S., Herman, P., Chien, S. W. D., Avanov, L., Dimmock, A. P., & Markidis, S. (2021). Automated Classification of Plasma Regions Using 3D Particle Energy Distributions. journal of geophysical research: Space Physics, 126(10), [e2021JA029620]. https://doi.org/10.1029/2021JA029620

Vancouver

Olshevsky V, Khotyaintsev YV, Lalti A, Divin A, Delzanno GL, Anderzén S et al. Automated Classification of Plasma Regions Using 3D Particle Energy Distributions. journal of geophysical research: Space Physics. 2021 Sep 10;126(10). e2021JA029620. https://doi.org/10.1029/2021JA029620

Author

Olshevsky, Vyacheslav ; Khotyaintsev, Yuri V. ; Lalti, Ahmad ; Divin, Andrey ; Delzanno, Gian Luca ; Anderzén, Sven ; Herman, Pawel ; Chien, Steven W.D. ; Avanov, Levon ; Dimmock, Andrew P. ; Markidis, Stefano. / Automated Classification of Plasma Regions Using 3D Particle Energy Distributions. In: journal of geophysical research: Space Physics. 2021 ; Vol. 126, No. 10.

BibTeX

@article{13b801e97bf9422caab016dec0d42c90,
title = "Automated Classification of Plasma Regions Using 3D Particle Energy Distributions",
abstract = "We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the Magnetospheric Multiscale Mission (MMS) on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is (Formula presented.) %. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.",
keywords = "MMS, machine learning, bow shock",
author = "Vyacheslav Olshevsky and Khotyaintsev, {Yuri V.} and Ahmad Lalti and Andrey Divin and Delzanno, {Gian Luca} and Sven Anderz{\'e}n and Pawel Herman and Chien, {Steven W.D.} and Levon Avanov and Dimmock, {Andrew P.} and Stefano Markidis",
note = "Publisher Copyright: {\textcopyright} 2021. The Authors.",
year = "2021",
month = sep,
day = "10",
doi = "10.1029/2021JA029620",
language = "English",
volume = "126",
journal = "Journal of Geophysical Research: Space Physics",
issn = "2169-9380",
publisher = "Wiley-Blackwell",
number = "10",

}

RIS

TY - JOUR

T1 - Automated Classification of Plasma Regions Using 3D Particle Energy Distributions

AU - Olshevsky, Vyacheslav

AU - Khotyaintsev, Yuri V.

AU - Lalti, Ahmad

AU - Divin, Andrey

AU - Delzanno, Gian Luca

AU - Anderzén, Sven

AU - Herman, Pawel

AU - Chien, Steven W.D.

AU - Avanov, Levon

AU - Dimmock, Andrew P.

AU - Markidis, Stefano

N1 - Publisher Copyright: © 2021. The Authors.

PY - 2021/9/10

Y1 - 2021/9/10

N2 - We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the Magnetospheric Multiscale Mission (MMS) on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is (Formula presented.) %. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.

AB - We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the Magnetospheric Multiscale Mission (MMS) on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is (Formula presented.) %. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.

KW - MMS

KW - machine learning

KW - bow shock

UR - http://www.scopus.com/inward/record.url?scp=85118179129&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/6616a845-a796-34d8-892f-3051445fd269/

U2 - 10.1029/2021JA029620

DO - 10.1029/2021JA029620

M3 - Article

AN - SCOPUS:85118179129

VL - 126

JO - Journal of Geophysical Research: Space Physics

JF - Journal of Geophysical Research: Space Physics

SN - 2169-9380

IS - 10

M1 - e2021JA029620

ER -

ID: 89311906