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., Heinzel, P., Kasparova, J. and Fletcher, L. (2021) On the importance of Ca II photoionisation by the hydrogen Lyman transitions in solar flare models. Monthly Notices of the Royal Astronomical Society, 507(2), pp. 1972-1978. (doi: 10.1093/mnras/stab2156)

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. and Fletcher, L. (2021) A machine-learning approach to correcting atmospheric seeing in solar flare observations. Monthly Notices of the Royal Astronomical Society, 501(2), pp. 2647-2658. (doi: 10.1093/mnras/staa3742)

Armstrong, J. A. and Fletcher, L. (2019) Fast solar image classification using deep learning and its importance for automation in solar physics. Solar Physics, 294, 80. (doi: 10.1007/s11207-019-1473-z)

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)