Inverse Rendering of Fusion Plasmas: Inferring Plasma Composition from Imaging Systems
<|reference_start|>Inverse Rendering of Fusion Plasmas: Inferring Plasma Composition from Imaging Systems: In this work, we develop a differentiable rendering pipeline for visualising plasma emission within tokamaks, and estimating the gradients of the emission and estimating other physical quantities. Unlike prior work, we are able to leverage arbitrary representations of plasma quantities and easily incorporate them into a non-linear optimisation framework. The efficiency of our method enables not only estimation of a physically plausible image of plasma, but also recovery of the neutral Deuterium distribution from imaging and midplane measurements alone. We demonstrate our method with three different levels of complexity showing first that a poloidal neutrals density distribution can be recovered from imaging alone, second that the distributions of neutral Deuterium, electron density and electron temperature can be recovered jointly, and finally, that this can be done in the presence of realistic imaging systems that incorporate sensor cropping and quantisation.<|reference_end|>
@article{öztürk2024inverse,
    title={Inverse Rendering of Fusion Plasmas: Inferring Plasma Composition from
  Imaging Systems},
    author={Ekin "Ozt"urk, Rob Akers, Stanislas Pamela, The MAST Team, Pieter
  Peers and Abhijeet Ghosh},
    journal={arXiv preprint arXiv:2408.07555},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07555},
    primaryClass={physics.plasm-ph cs.GR}
}