Metadata
Title
Astronomy & Astrophysics Group
Category
general
UUID
60ef094a7459421aa7a3bfd25cda0e7f
Source URL
https://www.astro.gla.ac.uk/?cat=11
Parent URL
https://www.astro.gla.ac.uk/?cat=13
Crawl Time
2026-03-11T06:09:26+00:00
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Astronomy & Astrophysics Group

Source: https://www.astro.gla.ac.uk/?cat=11 Parent: https://www.astro.gla.ac.uk/?cat=13

Ahmad Alharbi

PhD Student

Room 614\ School of Physics and Astronomy\ Kelvin Building\ University of Glasgow\ G12 8QQ\ Scotland\ Email:

Tel: +44 141 330 0855

Daniel Clarkson

PhD Student

Room 614\ School of Physics and Astronomy\ Kelvin Building\ University of Glasgow\ G12 8QQ\ Scotland\ Email: d.clarkson.2@research.gla.ac.uk

Tel: +44 141 330 0855

Sarah Paterson

PhD Student

Room 614\ School of Physics and Astronomy\ Kelvin Building\ University of Glasgow\ G12 8QQ\ Scotland\ Email: s.paterson.5@research.gla.ac.uk

Tel: +44 141 330 0855

David Millar

PhD Student

I am a Carnegie Trust PhD student, working with Professor Lyndsay Fletcher and studying oscillations in the Sun’s atmosphere, with particular focus on the chromosphere. I use mostly ground based imaging spectro-polarimetry in conjunction with space based data to look for evidence of oscillations and pulsations during solar flare activity. I am also interested in chromospheric waves and oscillations in quiet conditions, such as sunspot oscillations.

ORCID

Refereed Publications

David C L Millar, Lyndsay Fletcher, Ryan O Milligan, The effect of a solar flare on chromospheric oscillations, Monthly Notices of the Royal Astronomical Society, Volume 503, Issue 2, May 2021, Pages 2444–2456, DOI

Room 604\ School of Physics and Astronomy\ Kelvin Building\ University of Glasgow\ G12 8QQ\ Scotland\ Email: d.millar.2@research.gla.ac.uk\ davidclmillar@hotmail.co.uk

Tel: +44 141 330 2960\ @davidclmillar

Aaron W. Peat

I am working with Dr Nicolas Labrosse. My research is mainly concerned with solar prominences in the near ultraviolet wavelengths. I am currently working with MgII spectra from IRIS and using synthesised line profiles from NLTE code(s) to attempt to invert the atmosphere.

PhD Thesis

“Diagnostics of the thermodynamic properties of solar prominences”, Peat, A.W 2023. doi

Refereed Publications

  1. “Solar prominence diagnostics from non-LTE modelling of Mg II h&k line profiles”, Peat, A. W., Labrosse, N., Schmieder, B., and Barczynski, K. Astronomy and Astrophysics, vol. 653, 2021. doi
  2. “Spectro-imagery of an active tornado-like prominence: Formation and evolution”, Barczynski, K., Schmieder, B., Peat, A. W., Labrosse, N., Mein, P., and Mein, N.,  Astronomy and Astrophysics, vol. 653, 2021. doi
  3. “First high resolution interferometric observation of a solar prominence with ALMA“,  Labrosse, N., Rodger, A. S., Radziszewski, K., Rudawy, P.,  Antolin, P., Fletcher, L., Levens, P. J., Peat, A. W., Schmieder, B., and Simões, P. J. A., Monthly Notices of the Royal Astronomical Society, vol. 513, 2022. doi

Room 604\ School of Physics and Astronomy\ Kelvin Building\ University of Glasgow\ G12 8QQ\ Scotland

[Email](mailto:a.peat.1@research.gla.ac.uk)GitHub\ Orcid ID: 0000-0003-3085-2936\ Tel:  +44 141 330 2960

Kristopher Cooper

PhD Student

Room 604\ School of Physics and Astronomy\ Kelvin Building\ University of Glasgow\ G12 8QQ\ Scotland\ Email: k.cooper.2@research.gla.ac.uk

Tel: +44 141 330 2960

John Armstrong

PhD Student

I am working with Prof. Lyndsay Fletcher on the implementation of machine learning algorithms in solar observations. My main focus is on flare spectropolarimetry and how machine learning techniques can aid the data analysis process for carrying out chromospheric magnetic field diagnostics in a flaring atmosphere.

I have mainly applied supervised and unsupervised deep learning models to different solar aspects:

  1. Deep convolutional neural network (CNN) for solar feature detection

  2. Deep learning for correcting for atmospheric seeing in solar flare observations.

  3. Invertible neural network (INN) for the inversion of solar flare line profiles.

A C.V. can be found here.

Github

orcid.org/0000-0003-1589-9365

Refereed Publications

  1. “Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics”, J.A. Armstrong & L. Fletcher, Solar Physics, vol. 294:80, (2019). [doi] arXiv
  2. “RADYNVERSION: Learning to Invert a Solar Flare Atmosphere with Invertible Neural Networks”, C.M.J. Osborne, J.A. Armstrong & L. Fletcher, The Astrophysical Journal, vol. 873 (2), (2019). [doi] arXiv

Other Publications

  1. “Deep learning for the Sun”, J.A. Armstrong, C.M.J. Osborne & L. Fletcher, Astronomy & Geophysics, vol. 61, issue 3, June 2020, Pages 3.34–3.39 [doi]

Room 604\ School of Physics and Astronomy\ Kelvin Building\ University of Glasgow\ G12 8QQ\ Scotland\ Email: j.armstrong.2@research.gla.ac.uk

Tel: +44 141 330 2960

Christopher Osborne

PhD Student

Room ???\ School of Physics and Astronomy\ Kelvin Building\ University of Glasgow\ G12 8QQ\ Scotland\ Email:

Tel: +44 141 330 ????\ Fax: +44 141 330 8600