Contact person: Tiago Pereira
Keywords: Sun, stars, machine learning, magnetohydrodynamics, radiative transfer
Research group: Rosseland Centre for Solar Physics
Institute of Theoretical Astrophysics
Understanding the Sun is of crucial importance to understand other stars, the occurrence of life-supporting exoplanets, and predicting powerful storms that can be affect communications and life on Earth. Solar physics is on the cusp of a revolution made possible by a new generation of advanced telescopes and simulations reaching exascales. However, existing methods are no longer sufficient to make the most out of these new tools. The latest telescopes generate highly-dimensional data at volumes larger than 3 TB per hour, and 3D MHD simulations are too large to analyse in a single workstation. We are interested in ground-breaking approaches making use of machine learning and data mining techniques to enable a meaningful analysis of state-of-the-art solar observations and simulations. We invite research proposals that may combine traditional and machine learning methods to work in the topics listed below (or closely related).
Methodological research topics:
- Data mining: spectral clustering at 5-10 dimensions
- Data mining: visualisation of highly dimensional data sets
- Data denoising: fast processing of images to remove atmospheric perturbations
- Super-resolution spectral imaging: enhance datasets to transcend the diffraction limit of one or more sources
- Deep learning: accelerate MHD simulations
- Deep learning: accelerate 3D radiative transfer and ray tracing
Topics in solar physics:
- Understanding the formation of spectral lines in 3D simulations via spectral clustering
- Near real time image deconvolution of ground-based observations
- Combine and augment spectral data from multiple telescopes
- Enable 3D non-LTE spectral synthesis of large datasets to compare observations and simulations
- Use deep learning to predict surface magnetic field configurations for 3D MHD simulations
- Use deep learning to accelerate 3D radiative transfer in MHD simulations"
External partners:
- Instituto de Astrofísica de Canarias
- University of Glasgow
- Lockheed Martin Solar and Astrophysics Laboratory (LMSAL)
