Martian bow shock and magnetic pileup boundary models based on machine learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10484600" target="_blank" >RIV/00216208:11320/24:10484600 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=5tHQJVvKhd" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=5tHQJVvKhd</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asr.2024.03.030" target="_blank" >10.1016/j.asr.2024.03.030</a>

Result language

angličtina

Original language name

Martian bow shock and magnetic pileup boundary models based on machine learning

Original language description

Traditional Martian bow shock and magnetic pileup (magnetopause) boundary models are based on the fitting of free parameters in a prescribed formula. The form of the formula, fitted data, and considered controlling parameters distinguish the individual models from each other. However, all these models have one thing in common: the shape of the boundary and the parametric dependence assumed are fixed by the prescribed formula. The fitted data set typically consists of individual identified boundary crossings. This approach can suffer from a significant bias, as the boundary crossings are more likely to be identified in regions where a spacecraft spends more time. In this study, we use an automated region classification of the data measured by the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft to solar wind, magnetosheath, or magnetosphere. This is achieved by applying the Support Vector Machine method to individual spacecraft half-orbits (from periapsis to apoapsis or vice versa). Two different models of the locations of the bow shock and magnetic pileup boundaries are then constructed based on neural networks: i) a model trained using the classified data, and ii) a model trained using individual identified boundary crossings. As compared to formal empirical modeling efforts, the neural network models do not assume any prescribed shape/distance formula. Optimal model parameterization (considering the solar wind dynamic pressure, solar ionizing flux, crustal magnetic field magnitude, Alfve&lt;acute accent&gt;n Mach number, and interplanetary magnetic field magnitude) is discussed and the model performance evaluated. (c) 2024 COSPAR. Published by Elsevier B.V. All rights reserved.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10305 - Fluids and plasma physics (including surface physics)

Project

<a href="/en/project/LUAUS23152" target="_blank" >LUAUS23152: Electrodynamics of magnetospheres and ionospheres of the Earth, Jupiter and Mars</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Advances in Space Research

ISSN

0273-1177

e-ISSN

1879-1948

Volume of the periodical

73

Issue of the periodical within the volume

12

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

6298-6309

UT code for WoS article

001238205700001

EID of the result in the Scopus database

2-s2.0-85188747642