KIND: A Kalman-Inspired Adaptive Estimator for SRF Cavity Detuning
This work addresses the need for accurate detuning estimation in superconducting RF cavities, which is critical for efficient accelerator operation and stable beam conditions.
KIND introduces a data-driven estimator for SRF cavity detuning that combines Dynamic Mode Decomposition with a Transformer to handle both stationary and transient dynamics, achieving improved accuracy over classical Kalman filtering on operational data.
Superconducting radio frequency cavities with a high quality factor enable energy-efficient accelerator operation but are very sensitive to mechanical disturbances that detune their resonance. Accurate detuning estimation is therefore essential for efficient resonance control and stable beam conditions. This paper introduces Kalman-Inspired Neural Decomposition (KIND), a data-driven estimator that fuses a Dynamic Mode Decomposition model for stationary modal behavior with a Transformer-based predictor for transient dynamics. KIND further outputs learned uncertainty signals that indicate regime changes, enabling anomaly detection. Using operational cavity data, we compare KIND with a classical Kalman filtering baseline and discuss its potential as a foundation for future uncertainty-aware, forecast-based control.