Speaker
Description
Machine learning (ML) is increasingly recognized as a fundamental tool in modern accelerator physics, offering new capabilities to complement and extend traditional analytical approaches. A structured programme of ML studies has been initiated at CLEAR (CERN Linear Electron Accelerator for Research), covering three principal directions: beam diagnostics, charge forecasting, and automated tuning via reinforcement learning. Deep learning models were explored to reconstruct transverse beam profiles from indirect measurements, offering a pathway toward radiation-resistant diagnostics. Classical and deep learning approaches were benchmarked for charge prediction, demonstrating the potential for accurate real-time beam characterization. A reinforcement learning framework was interfaced with the CERN control infrastructure, enabling proof-of-principle autonomous beam steering through iterative magnet corrections. These results demonstrate the feasibility of integrating ML into routine CLEAR operations and position the facility as an attractive testbed for the development and validation of machine learning techniques for particle accelerators.
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