Speaker
Sara Abbasi
(Faculty of Nuclear Sciences and Physical Engineering, CTU in Prague)
Description
This work explores the application of Convolutional Neural Networks (CNNs) for tomographic reconstruction of visible plasma radiation distribution at the GOLEM tokamak. The training datasets are generated from emissivity phantoms of the poloidal cross-section together with synthetic measurements from two visible cameras. CNNs are employed for their capability to capture local image patterns in the camera data and reconstruct complex radiation profiles. The study examines how the design of the training data influences model performance, with the aim of developing optimized strategies for accurate and reliable CNN-based tomographic reconstruction in plasma diagnostics.
Primary authors
Sara Abbasi
(Faculty of Nuclear Sciences and Physical Engineering, CTU in Prague)
Ondrej Ficker
(Faculty of Nuclear Sciences and Physical Engineering, CTU in Prague)
Jakub Chlum
(Faculty of Nuclear Sciences and Physical Engineering, CTU in Prague)
Vojtech Svoboda
(Faculty of Nuclear Sciences and Physical Engineering, CTU in Prague)
