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Improving Coronal Magnetic Field Models Using Image Optimization. / Jones, Shaela I.; Uritsky, Vadim M.; Davila, Joseph M.; Troyan, Vladimir N.

в: Astrophysical Journal, Том 896, № 1, 57, 10.06.2020.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Jones, SI, Uritsky, VM, Davila, JM & Troyan, VN 2020, 'Improving Coronal Magnetic Field Models Using Image Optimization', Astrophysical Journal, Том. 896, № 1, 57. https://doi.org/10.3847/1538-4357/ab8cb9

APA

Jones, S. I., Uritsky, V. M., Davila, J. M., & Troyan, V. N. (2020). Improving Coronal Magnetic Field Models Using Image Optimization. Astrophysical Journal, 896(1), [57]. https://doi.org/10.3847/1538-4357/ab8cb9

Vancouver

Jones SI, Uritsky VM, Davila JM, Troyan VN. Improving Coronal Magnetic Field Models Using Image Optimization. Astrophysical Journal. 2020 Июнь 10;896(1). 57. https://doi.org/10.3847/1538-4357/ab8cb9

Author

Jones, Shaela I. ; Uritsky, Vadim M. ; Davila, Joseph M. ; Troyan, Vladimir N. / Improving Coronal Magnetic Field Models Using Image Optimization. в: Astrophysical Journal. 2020 ; Том 896, № 1.

BibTeX

@article{0394321793484f858faf2a03352512cc,
title = "Improving Coronal Magnetic Field Models Using Image Optimization",
abstract = "We have reported previously on our development and testing of a new method for using coronal images to improve coronal magnetic field models. In this technique, which we call image-optimization, coronal magnetic field models are extrapolated from synoptic photospheric magnetograms. The resulting models are then compared to morphological constraints derived from images of the solar corona, and the photospheric magnetograms are perturbed iteratively via an optimization algorithm to achieve optimal agreement with the image-based constraints. Here we present results from the first application of this technique using Mauna Loa Solar Observatory K-Coronagraph images and Global Oscillation Network Group synoptic magnetograms to create optimized models for two time periods, 2014 November 16-29 and 2016 May 16-29. We find that for both time periods the optimization algorithm converges well and results in better agreement between the model and the images, relatively small changes to the synoptic magnetogram, and an overall increase in the amount of open magnetic flux.",
keywords = "SOLAR, INTERPLANETARY, UNCERTAINTIES, MAPS",
author = "Jones, {Shaela I.} and Uritsky, {Vadim M.} and Davila, {Joseph M.} and Troyan, {Vladimir N.}",
note = "Publisher Copyright: {\textcopyright} 2020. The American Astronomical Society. All rights reserved.. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2020",
month = jun,
day = "10",
doi = "10.3847/1538-4357/ab8cb9",
language = "English",
volume = "896",
journal = "Astrophysical Journal",
issn = "0004-637X",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Improving Coronal Magnetic Field Models Using Image Optimization

AU - Jones, Shaela I.

AU - Uritsky, Vadim M.

AU - Davila, Joseph M.

AU - Troyan, Vladimir N.

N1 - Publisher Copyright: © 2020. The American Astronomical Society. All rights reserved.. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/6/10

Y1 - 2020/6/10

N2 - We have reported previously on our development and testing of a new method for using coronal images to improve coronal magnetic field models. In this technique, which we call image-optimization, coronal magnetic field models are extrapolated from synoptic photospheric magnetograms. The resulting models are then compared to morphological constraints derived from images of the solar corona, and the photospheric magnetograms are perturbed iteratively via an optimization algorithm to achieve optimal agreement with the image-based constraints. Here we present results from the first application of this technique using Mauna Loa Solar Observatory K-Coronagraph images and Global Oscillation Network Group synoptic magnetograms to create optimized models for two time periods, 2014 November 16-29 and 2016 May 16-29. We find that for both time periods the optimization algorithm converges well and results in better agreement between the model and the images, relatively small changes to the synoptic magnetogram, and an overall increase in the amount of open magnetic flux.

AB - We have reported previously on our development and testing of a new method for using coronal images to improve coronal magnetic field models. In this technique, which we call image-optimization, coronal magnetic field models are extrapolated from synoptic photospheric magnetograms. The resulting models are then compared to morphological constraints derived from images of the solar corona, and the photospheric magnetograms are perturbed iteratively via an optimization algorithm to achieve optimal agreement with the image-based constraints. Here we present results from the first application of this technique using Mauna Loa Solar Observatory K-Coronagraph images and Global Oscillation Network Group synoptic magnetograms to create optimized models for two time periods, 2014 November 16-29 and 2016 May 16-29. We find that for both time periods the optimization algorithm converges well and results in better agreement between the model and the images, relatively small changes to the synoptic magnetogram, and an overall increase in the amount of open magnetic flux.

KW - SOLAR

KW - INTERPLANETARY

KW - UNCERTAINTIES

KW - MAPS

UR - http://www.scopus.com/inward/record.url?scp=85091283739&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/bd2512d3-9b75-3d77-816f-92a372169baa/

U2 - 10.3847/1538-4357/ab8cb9

DO - 10.3847/1538-4357/ab8cb9

M3 - Article

AN - SCOPUS:85091283739

VL - 896

JO - Astrophysical Journal

JF - Astrophysical Journal

SN - 0004-637X

IS - 1

M1 - 57

ER -

ID: 70789637