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. Conditional Generative Adversarial Network (cGAN) 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.
- “RADYNVERSION: Learning to Invert a Solar Flare Atmosphere with Invertible Neural Networks”, C.M.J. Osborne, J.A. Armstrong & L. Fletcher, The Astrophysical Journal, (2019, submitted). arXiv
School of Physics and Astronomy
University of Glasgow
Tel: +44 141 330 2960