In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresis in ARMCO pure iron, an important soft magnetic material in particle accelerator magnets. A recurrent neural network coupled with Preisach play operators is proposed, along with a novel validation method for the identification of the model’s parameters. The proposed model is found to predict the magnetic flux density of ARMCO pure iron with a Normalised Root Mean Square Error (NRMSE) better than 0.7%, when trained with just six different hysteresis loops. The model is evaluated using ramp-rates not used in the training procedure, which shows the ability of the model to predict data which has not been measured. The results demonstrate that the Preisach model based on a recurrent neural network can accurately describe ferromagnetic dynamic hysteresis when trained with a limited amount of data, showing the model’s potential in the field of materials science.

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In this paper, we present a measurement model for estimating the magnetic field of a synchrotron-type particle accelerator, based on sensors installed in a reference magnet. The model combines the calibration of the individual sensors with the experimental characterization of the magnets to infer, in absolute terms, the value of the average field in the ring, as needed for the real-time feedback control of the accelerator. Implementation of this model at the extra low energy antiproton (ELENA) ring at the European Organization for Nuclear Research (CERN) is used as a case study. We describe first the measurement setup and method, followed by the detailed definition of the model, along with its parameters and an evaluation of their value and uncertainty. Next, we assess the combined uncertainty of the whole measurement chain. Finally, we discuss the results obtained so far during the machine commissioning phase and outline our plans for future improvement.

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The European XFEL is currently the only high-repetition rate hard X-ray free electron laser (FEL) facility in operation worldwide. We significantly improved its capabilities by installing a cascaded Hard X-ray Self-Seeding (HXRSS) system, composed of two single-crystal monochromators. With this system, mJ-level pulses in the photon energy range of 6 -14keV with a bandwidth around 1eV (corresponding to about 1mJ/eV spectral density) were generated. Combined with the burst-mode, multi-MHz repetition rate of the European XFEL accelerator, the cascaded HXRSS setup provides two orders of magnitude higher average spectral brightness than any other FEL facility. At 2.25 MHz repetition rate and photon energies in the 6-7 keV range, we observed for the first time heat-load effects on the HXRSS crystals, substantially altering the spectra of subsequent X-ray pulses. Using the cascaded self-seeding scheme, we successfully reduced this effect to below detection level. These results open up exciting possibilities in a wide range of scientific fields, exploiting the extreme brightness and the narrow bandwidth of HXRSS pulses.

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The precise knowledge of the magnetic field produced by dipole magnets is critical to the operation of a synchrotron. Real-time measurement systems may be required, especially in the case of iron-dominated electromagnets with strong non-linear effects, to acquire the magnetic field and feed it back to various users. This work concerns the design and implementation of a new measurement system of this kind currently being deployed throughout the European Organization for Nuclear Research (CERN) accelerator complex. We first discuss the measurement principle, the general system architecture and the technology employed, focusing in particular on the most critical and specialized components developed, that is, the field marker trigger generator and the magnetic flux integrator. We then present the results of a detailed metrological characterization of the integrator, including the aspects of drift estimation and correction, as well as the absolute gain calibration and frequency response. We finally discuss the latency of the whole acquisition chain and present an outline of future work to improve the capabilities of the system.