Beam Energy Forecasting using Machine Learning at the CLEAR accelerator

In particle accelerators, accurate and stable beam parameters are crucial for experimental success. Traditional methods for measuring parameters like beam energy often rely on invasive techniques that disrupt experiments. This paper presents a novel, non-invasive machine learning-based approach to predict beam energy using parasitic measurements, enabling real-time estimation without interference. The method employs a predictive model optimized for one-step-ahead forecasting and uses time-series data decomposition to handle complex beam energy dynamics. Recursive prediction strategies allow the model to anticipate future variations autonomously. Preliminary results from experiments at the CLEAR accelerator demonstrate the model’s ability to capture both slow trends and rapid energy shifts, adapting to diverse experimental needs. These findings showcase the potential of machine learning to measure beam energy, offering a real-time, non-destructive alternative to conventional methods. This approach promises significant advancements in accelerator-based applications, especially where destructive techniques are impractical.