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My research primarily focuses on the modelling and interpretation of optical spectral lines from solar flares. I specialise in radiative transfer with time-dependent atomic populations outside of local thermodynamic equilibrium, and also the application of machine learning to invert this problem in observations in a computationally tractable manner.
Peat, A. W., Osborne, C. M.J. and Heinzel, P. (2024) Doppler dimming and brightening effects in solar prominences. Monthly Notices of the Royal Astronomical Society: Letters, (doi: 10.1093/mnrasl/slae055) (Accepted for Publication)
Jenkins, J. M., Osborne, C. M. J., Qiu, Y., Keppens, R. and Li, C. (2024) The bright rim prominences according to 2.5D radiative transfer. Astrophysical Journal Letters, 964(2), L34. (doi: 10.3847/2041-8213/ad3423)
Osborne, C. M. J. and Fletcher, L. (2022) Flare kernels may be smaller than you think: modelling the radiative response of chromospheric plasma adjacent to a solar flare. Monthly Notices of the Royal Astronomical Society, 516(4), pp. 6066-6074. (doi: 10.1093/mnras/stac2570)
Osborne, C. M.J. and Milić, I. (2021) The Lightweaver framework for nonlocal thermal equlibrum radiative transfer in Python. Astrophysical Journal, 917(1), 14. (doi: 10.3847/1538-4357/ac02be)
Armstrong, J. A. , Osborne, C. M.J. and Fletcher, L. (2020) Deep learning for the Sun. Astronomy and Geophysics, 61(3), 3.34-3.39. (doi: 10.1093/astrogeo/ataa044)
Osborne, C. M.J. and Simoes, P. J.A. (2019) Thyr: a volumetric ray-marching tool for simulating microwave emission. Monthly Notices of the Royal Astronomical Society, 485(3), pp. 3386-3397. (doi: 10.1093/mnras/stz660)
Osborne, C. M.J., Armstrong, J. A. and Fletcher, L. (2019) RADYNVERSION: Learning to invert a solar flare atmosphere with invertible neural networks. Astrophysical Journal, 873, 128. (doi: 10.3847/1538-4357/ab07b4)
Peat, A. W., Osborne, C. M.J. and Heinzel, P. (2024) Doppler dimming and brightening effects in solar prominences. Monthly Notices of the Royal Astronomical Society: Letters, (doi: 10.1093/mnrasl/slae055) (Accepted for Publication)
Jenkins, J. M., Osborne, C. M. J., Qiu, Y., Keppens, R. and Li, C. (2024) The bright rim prominences according to 2.5D radiative transfer. Astrophysical Journal Letters, 964(2), L34. (doi: 10.3847/2041-8213/ad3423)
Osborne, C. M. J. and Fletcher, L. (2022) Flare kernels may be smaller than you think: modelling the radiative response of chromospheric plasma adjacent to a solar flare. Monthly Notices of the Royal Astronomical Society, 516(4), pp. 6066-6074. (doi: 10.1093/mnras/stac2570)
Osborne, C. M.J. and Milić, I. (2021) The Lightweaver framework for nonlocal thermal equlibrum radiative transfer in Python. Astrophysical Journal, 917(1), 14. (doi: 10.3847/1538-4357/ac02be)
Armstrong, J. A. , Osborne, C. M.J. and Fletcher, L. (2020) Deep learning for the Sun. Astronomy and Geophysics, 61(3), 3.34-3.39. (doi: 10.1093/astrogeo/ataa044)
Osborne, C. M.J. and Simoes, P. J.A. (2019) Thyr: a volumetric ray-marching tool for simulating microwave emission. Monthly Notices of the Royal Astronomical Society, 485(3), pp. 3386-3397. (doi: 10.1093/mnras/stz660)
Osborne, C. M.J., Armstrong, J. A. and Fletcher, L. (2019) RADYNVERSION: Learning to invert a solar flare atmosphere with invertible neural networks. Astrophysical Journal, 873, 128. (doi: 10.3847/1538-4357/ab07b4)
