Multi-instrument Bayesian reconstruction of plasma shape evolution in C-2W experiment Skip to main content Explore our many areas of focus Explore all research areas Applied AI & sciences Earth AI Health AI Science AI Sustainability & crisis resilience Foundational ML & algorithms Algorithms & theory Information retrieval Machine intelligence Machine perception Natural language processing People, systems & quantum AI Human-computer interaction and visualization Networking Quantum AI Responsible AI Anti abuse Software engineering Software systems Learn More Publications Projects Building a collaborative ecosystem Datasets Access high-quality datasets to accelerate your research. Tools & services Explore our latest AI models and products. Open source Discover open-source code and collaborate with the community. Shaping the future together See all programs Faculty programs Participating in the academic research community through meaningful engagement with university faculty. 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Google Research Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Research Resources Conferences & events Careers Blog About Search Home Publications Multi-instrument Bayesian reconstruction of plasma shape evolution in C-2W experiment Anton Kast Erik Trask Hiroshi Gota Ian Langmore Jesus Romero John Platt Michael Dikovsky Peter Norgaard Rob von Behren Scott Davidson Geraedts Ted Baltz Tom Madams Physics of Plasmas (2021) Google Scholar Copy Bibtex Abstract We determined the time-dependent geometry including high-frequency oscillations of the plasma density in TAE’s C2W experiment. This was done as a joint Bayesian reconstruction from a 14-chord FIR interferometer in the midplane, 32 Mirnov probes at the periphery, and 8 shine-through detectors at the targets of the neutral beams. For each point in time we recovered, with credibility intervals: the radial density profile of the plasma; bulk plasma displacement; amplitudes, frequencies and phases of the azimuthal modes n=1 to n=4. Also reconstructed were the radial profiles of the deformations associated with each of the azimuthal modes. Bayesian posterior sampling was done via Hamiltonian Monte Carlo with custom preconditioning. This gave us a comprehensive uncertainty quantification of the reconstructed values, including correlations and some understanding of multimodal posteriors. This method was applied to thousands of experimental shots on C-2W, producing a rich data set for analysis of plasma performance. Meet the teams driving innovation Our teams advance the state of the art through research, systems engineering, and collaboration across Google. 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