Time-drift Aware RF Optimization with Machine Learning Techniques

TL;DR

This paper explores machine learning algorithms for automated RF tuning in Fermilab's Linac, aiming to adapt to environmental and operational drifts to optimize beam energy and phase stability.

Contribution

It introduces a time-drift aware ML approach for RF parameter optimization, addressing environmental variations affecting accelerator performance.

Findings

ML algorithms effectively adapt to environmental drifts.

Improved stability in beam energy and phase oscillations.

Potential for automated, real-time RF tuning.

Abstract

The Fermilab Linac delivers 400 MeV H- beam to the rest of the accelerator chain. Providing stable intensity, energy, and emittance is key since it directly affects downstream machines. To operate high current beam, accelerators must minimize uncontrolled particle loss; this can be accomplished by minimizing beam longitudinal emittance via RF parameter optimization. However, RF tuning is required daily since the resonance frequency of the accelerating cavities is affected by ambient temperature and humidity variations and thus drifts with time. In addition, the energy and phase space distribution of particles emerging from the ion source are subject to fluctuations. Such drift is not unique to Fermilab, but rather affects most laboratories. We are exploring machine learning (ML) algorithms for automated RF tuning for 2 objectives: optimization of Linac output energy and phase oscillation correction, with an emphasis on time-drift aware modeling that can account for conditions changing over time.