KIND: A Kalman-Inspired Adaptive Estimator for SRF Cavity Detuning
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914558086479872 |
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| author | Maalberg, Andrei Neumann, Axel Echevarria, Pablo Ushakov, Andriy Knobloch, Jens |
| author_facet | Maalberg, Andrei Neumann, Axel Echevarria, Pablo Ushakov, Andriy Knobloch, Jens |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11957 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | KIND: A Kalman-Inspired Adaptive Estimator for SRF Cavity Detuning Maalberg, Andrei Neumann, Axel Echevarria, Pablo Ushakov, Andriy Knobloch, Jens Systems and Control 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. |
| title | KIND: A Kalman-Inspired Adaptive Estimator for SRF Cavity Detuning |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.11957 |