Speaker
Description
The Liverpool Centre for Doctoral Training in Innovation in Data Intensive Science (LIV.INNO) has made significant progress in applying data-intensive methods to accelerator research. This contribution presents research outcomes from the center with a focus on two key projects. The first focuses on optimizing 3D imaging for medical and industrial applications, integrating Monte Carlo simulations and advanced collimation techniques to enhance low-dose, portable X-ray systems, with implications for wider accelerator diagnostics. The second leverages deep learning models to reconstruct transverse beam distributions at CERN, addressing challenges in image distortion from multimode optical fibers under high-radiation conditions. The results will be connected with wider progress in the area of machine learning and artificial intelligence for particle accelerators. Furthermore, the contribution will summarize the outcomes of the STFC Summer School in Data Intensive Science and the LIV.INNO industry showcase, both of which were held in Liverpool, UK in 2024.
Funding Agency
This work was supported by the Science and Technology Facilities Council
(STFC) under grant agreement ST/W006766/1.
| Region represented | Europe |
|---|---|
| Paper preparation format | Word |
