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.
@article{arxiv.2605.11957,
title = {KIND: A Kalman-Inspired Adaptive Estimator for SRF Cavity Detuning},
author = {Andrei Maalberg and Axel Neumann and Pablo Echevarria and Andriy Ushakov and Jens Knobloch},
journal= {arXiv preprint arXiv:2605.11957},
year = {2026}
}
Comments
Accepted for publication at the 2026 IFAC World Congress (IFAC 2026). 6 pages