Accurately constraining velocity information from spectral imaging observations using machine learning techniques: Fitting velocities with machine learning

Conor D. MacBride; David B. Jess; Samuel D.T. Grant; Elena Khomenko; Peter H. Keys; Marco Stangalini

doi:10.1098/rsta.2020.0171

Accurately constraining velocity information from spectral imaging observations using machine learning techniques: Fitting velocities with machine learning

Conor D. MacBride, David B. Jess, Samuel D.T. Grant, Elena Khomenko, Peter H. Keys, Marco Stangalini

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

11 Citations (Scopus)

181 Downloads (Pure)

Abstract

Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evolving and, hence, contain multiple spectral components in their constituent line profiles. Here, we present a novel method that employs machine learning techniques to identify the underlying components present within observed spectral lines, before subsequently constraining the constituent profiles through single or multiple Voigt fits. Our method allows active and quiescent components present in spectra to be identified and isolated for subsequent study. Lastly, we employ a Ca ii 8542 Å spectral imaging dataset as a proof-of-concept study to benchmark the suitability of our code for extracting two-component atmospheric profiles that are commonly present in sunspot chromospheres. Minimization tests are employed to validate the reliability of the results, achieving median reduced χ 2-values equal to 1.03 between the observed and synthesized umbral line profiles.

This article is part of the Theo Murphy meeting issue 'High-resolution wave dynamics in the lower solar atmosphere'.

Original language	English
Article number	20200171
Number of pages	23
Journal	Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume	379
Issue number	2190
Early online date	21 Dec 2020
DOIs	https://doi.org/10.1098/rsta.2020.0171
Publication status	Published - 08 Feb 2021

Bibliographical note

Funding Information:
Data accessibility. The data used in this paper are from the observing campaign entitled ‘Nanoflare Activity in the Lower Solar Atmosphere’ (NSO-SP proposal T1020; principal investigator: DBJ), which employed the ground-based Dunn Solar Telescope, USA, during August 2014. The Dunn Solar Telescope at Sacramento Peak/NM was operated by the National Solar Observatory (NSO). NSO is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under cooperative agreement with the National Science Foundation (NSF). Additional supporting observations were obtained from the publicly available NASA’s Solar Dynamics Observatory (https://sdo.gsfc.nasa.gov) data archive, which can be accessed via http://jsoc.stanford.edu/ajax/lookdata.html. The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. Authors’ contributions. C.D.M. and D.B.J. conceived of and designed the study. D.B.J. carried out the experiments. C.D.M. performed the data reduction and scientific analysis, with assistance from D.B.J., S.D.T.G., P.H.K. and M.S. C.D.M. drafted the manuscript, with theoretical input provided by E.K. All authors read and approved the manuscript. Competing interests. We declare we have no competing interests. Funding. This work was supported by The Department for the Economy (Northern Ireland) through their postgraduate research studentship and an Invest NI and Randox Laboratories Ltd. Research & Development (grant no. 059RDEN-1). Acknowledgements. C.D.M. would like to thank the Northern Ireland Department for the Economy for the award of a PhD studentship. D.B.J. and S.D.T.G. are grateful to Invest NI and Randox Laboratories Ltd. for the award of a Research & Development that allowed the computational techniques employed to be developed. The authors wish to acknowledge scientific discussions with the Waves in the Lower Solar Atmosphere (WaLSA; www.WaLSA.team) team, which is supported by the Research Council of Norway (project no. 262622), and The Royal Society through the award of funding to host the Theo Murphy Discussion Meeting ‘High-resolution wave dynamics in the lower solar atmosphere’ (grant no. Hooke18b/SCTM).

Publisher Copyright:
© 2020 The Author(s).

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

methods: Statistical
Sun: Atmosphere
Sun: Chromosphere
Sun: Photosphere
sunspots
techniques: Spectroscopic

ASJC Scopus subject areas

General Mathematics
General Engineering
General Physics and Astronomy

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10.1098/rsta.2020.0171Licence: Unspecified

Accurately constraining velocity information from spectral imaging observations using machine learning techniques
Copyright 2020 the authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited
Accepted author manuscript, 2.27 MBLicence: CC BY

Wave and energy characterisation in the atmosphere of sunspots
MacBride, C. (Author), Jess, D. (Supervisor) & Mathioudakis, M. (Supervisor), Jul 2023
Student thesis: Doctoral Thesis › Doctor of Philosophy

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MacBride, C. D., Jess, D. B., Grant, S. D. T., Khomenko, E., Keys, P. H., & Stangalini, M. (2021). Accurately constraining velocity information from spectral imaging observations using machine learning techniques: Fitting velocities with machine learning. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2190), Article 20200171. https://doi.org/10.1098/rsta.2020.0171

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title = "Accurately constraining velocity information from spectral imaging observations using machine learning techniques: Fitting velocities with machine learning",

abstract = "Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evolving and, hence, contain multiple spectral components in their constituent line profiles. Here, we present a novel method that employs machine learning techniques to identify the underlying components present within observed spectral lines, before subsequently constraining the constituent profiles through single or multiple Voigt fits. Our method allows active and quiescent components present in spectra to be identified and isolated for subsequent study. Lastly, we employ a Ca ii 8542 {\AA} spectral imaging dataset as a proof-of-concept study to benchmark the suitability of our code for extracting two-component atmospheric profiles that are commonly present in sunspot chromospheres. Minimization tests are employed to validate the reliability of the results, achieving median reduced χ 2-values equal to 1.03 between the observed and synthesized umbral line profiles. This article is part of the Theo Murphy meeting issue 'High-resolution wave dynamics in the lower solar atmosphere'. ",

keywords = "methods: Statistical, Sun: Atmosphere, Sun: Chromosphere, Sun: Photosphere, sunspots, techniques: Spectroscopic",

author = "MacBride, {Conor D.} and Jess, {David B.} and Grant, {Samuel D.T.} and Elena Khomenko and Keys, {Peter H.} and Marco Stangalini",

note = "Funding Information: Data accessibility. The data used in this paper are from the observing campaign entitled {\textquoteleft}Nanoflare Activity in the Lower Solar Atmosphere{\textquoteright} (NSO-SP proposal T1020; principal investigator: DBJ), which employed the ground-based Dunn Solar Telescope, USA, during August 2014. The Dunn Solar Telescope at Sacramento Peak/NM was operated by the National Solar Observatory (NSO). NSO is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under cooperative agreement with the National Science Foundation (NSF). Additional supporting observations were obtained from the publicly available NASA{\textquoteright}s Solar Dynamics Observatory (https://sdo.gsfc.nasa.gov) data archive, which can be accessed via http://jsoc.stanford.edu/ajax/lookdata.html. The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. Authors{\textquoteright} contributions. C.D.M. and D.B.J. conceived of and designed the study. D.B.J. carried out the experiments. C.D.M. performed the data reduction and scientific analysis, with assistance from D.B.J., S.D.T.G., P.H.K. and M.S. C.D.M. drafted the manuscript, with theoretical input provided by E.K. All authors read and approved the manuscript. Competing interests. We declare we have no competing interests. Funding. This work was supported by The Department for the Economy (Northern Ireland) through their postgraduate research studentship and an Invest NI and Randox Laboratories Ltd. Research & Development (grant no. 059RDEN-1). Acknowledgements. C.D.M. would like to thank the Northern Ireland Department for the Economy for the award of a PhD studentship. D.B.J. and S.D.T.G. are grateful to Invest NI and Randox Laboratories Ltd. for the award of a Research & Development that allowed the computational techniques employed to be developed. The authors wish to acknowledge scientific discussions with the Waves in the Lower Solar Atmosphere (WaLSA; www.WaLSA.team) team, which is supported by the Research Council of Norway (project no. 262622), and The Royal Society through the award of funding to host the Theo Murphy Discussion Meeting {\textquoteleft}High-resolution wave dynamics in the lower solar atmosphere{\textquoteright} (grant no. Hooke18b/SCTM). Publisher Copyright: {\textcopyright} 2020 The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

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MacBride, CD, Jess, DB , Grant, SDT, Khomenko, E, Keys, PH & Stangalini, M 2021, 'Accurately constraining velocity information from spectral imaging observations using machine learning techniques: Fitting velocities with machine learning', Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 379, no. 2190, 20200171. https://doi.org/10.1098/rsta.2020.0171

Accurately constraining velocity information from spectral imaging observations using machine learning techniques: Fitting velocities with machine learning. / MacBride, Conor D.; Jess, David B.; Grant, Samuel D.T. et al.
In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 379, No. 2190, 20200171, 08.02.2021.

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

TY - JOUR

T1 - Accurately constraining velocity information from spectral imaging observations using machine learning techniques: Fitting velocities with machine learning

AU - MacBride, Conor D.

AU - Jess, David B.

AU - Grant, Samuel D.T.

AU - Khomenko, Elena

AU - Keys, Peter H.

AU - Stangalini, Marco

N1 - Funding Information: Data accessibility. The data used in this paper are from the observing campaign entitled ‘Nanoflare Activity in the Lower Solar Atmosphere’ (NSO-SP proposal T1020; principal investigator: DBJ), which employed the ground-based Dunn Solar Telescope, USA, during August 2014. The Dunn Solar Telescope at Sacramento Peak/NM was operated by the National Solar Observatory (NSO). NSO is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under cooperative agreement with the National Science Foundation (NSF). Additional supporting observations were obtained from the publicly available NASA’s Solar Dynamics Observatory (https://sdo.gsfc.nasa.gov) data archive, which can be accessed via http://jsoc.stanford.edu/ajax/lookdata.html. The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. Authors’ contributions. C.D.M. and D.B.J. conceived of and designed the study. D.B.J. carried out the experiments. C.D.M. performed the data reduction and scientific analysis, with assistance from D.B.J., S.D.T.G., P.H.K. and M.S. C.D.M. drafted the manuscript, with theoretical input provided by E.K. All authors read and approved the manuscript. Competing interests. We declare we have no competing interests. Funding. This work was supported by The Department for the Economy (Northern Ireland) through their postgraduate research studentship and an Invest NI and Randox Laboratories Ltd. Research & Development (grant no. 059RDEN-1). Acknowledgements. C.D.M. would like to thank the Northern Ireland Department for the Economy for the award of a PhD studentship. D.B.J. and S.D.T.G. are grateful to Invest NI and Randox Laboratories Ltd. for the award of a Research & Development that allowed the computational techniques employed to be developed. The authors wish to acknowledge scientific discussions with the Waves in the Lower Solar Atmosphere (WaLSA; www.WaLSA.team) team, which is supported by the Research Council of Norway (project no. 262622), and The Royal Society through the award of funding to host the Theo Murphy Discussion Meeting ‘High-resolution wave dynamics in the lower solar atmosphere’ (grant no. Hooke18b/SCTM). Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/2/8

Y1 - 2021/2/8

N2 - Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evolving and, hence, contain multiple spectral components in their constituent line profiles. Here, we present a novel method that employs machine learning techniques to identify the underlying components present within observed spectral lines, before subsequently constraining the constituent profiles through single or multiple Voigt fits. Our method allows active and quiescent components present in spectra to be identified and isolated for subsequent study. Lastly, we employ a Ca ii 8542 Å spectral imaging dataset as a proof-of-concept study to benchmark the suitability of our code for extracting two-component atmospheric profiles that are commonly present in sunspot chromospheres. Minimization tests are employed to validate the reliability of the results, achieving median reduced χ 2-values equal to 1.03 between the observed and synthesized umbral line profiles. This article is part of the Theo Murphy meeting issue 'High-resolution wave dynamics in the lower solar atmosphere'.

AB - Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evolving and, hence, contain multiple spectral components in their constituent line profiles. Here, we present a novel method that employs machine learning techniques to identify the underlying components present within observed spectral lines, before subsequently constraining the constituent profiles through single or multiple Voigt fits. Our method allows active and quiescent components present in spectra to be identified and isolated for subsequent study. Lastly, we employ a Ca ii 8542 Å spectral imaging dataset as a proof-of-concept study to benchmark the suitability of our code for extracting two-component atmospheric profiles that are commonly present in sunspot chromospheres. Minimization tests are employed to validate the reliability of the results, achieving median reduced χ 2-values equal to 1.03 between the observed and synthesized umbral line profiles. This article is part of the Theo Murphy meeting issue 'High-resolution wave dynamics in the lower solar atmosphere'.

KW - methods: Statistical

KW - Sun: Atmosphere

KW - Sun: Chromosphere

KW - Sun: Photosphere

KW - sunspots

KW - techniques: Spectroscopic

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DO - 10.1098/rsta.2020.0171

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AN - SCOPUS:85098743985

SN - 1364-503X

VL - 379

JO - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

JF - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

IS - 2190

M1 - 20200171

ER -

MacBride CD, Jess DB , Grant SDT, Khomenko E, Keys PH, Stangalini M. Accurately constraining velocity information from spectral imaging observations using machine learning techniques: Fitting velocities with machine learning. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2021 Feb 8;379(2190):20200171. Epub 2020 Dec 21. doi: 10.1098/rsta.2020.0171

Accurately constraining velocity information from spectral imaging observations using machine learning techniques: Fitting velocities with machine learning

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Student theses

Wave and energy characterisation in the atmosphere of sunspots

Cite this