Tuesday, December 5, 2023, 12:00 pm — Videoconference / Virtual Event(videoconference link to be announced)
Traditional beam tomography techniques require hundreds of samples for a high-fidelity reconstruction. The performance of heavy ion accelerators, such as FRIB and ATLAS, may suffer from the drift of the accelerator, resulting in the need for efficient reconstruction using fewer samples before beam distribution changes significantly. This work uses a machine-learning approach to reconstruct the 2D or 4D phase space directly from 1D measurements.
Hosted by: Yuan Gao
19596 | INT/EXT | Events Calendar
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