Machine learning models are proposed to successfully detect heating from pressure measurements in synchrotron colliders. These models allow to analyze all the pressure measurements in the time ...available between two consecutive machine runs. The limits of simple heuristic-based algorithms arsing from noise and non-reproducibility are overcome by the proposed machine learning models. These models were trained, tested, and compared with an heuristic-based base-line approach. In particular, for the case of the CERN Large Hadron Collider (LHC), they reached better performance than base-line algorithms, both in precision and recall scores.
In this work, the design of precision voltage and current sources for a new quench detection system for the protection of superconducting power cables is proposed. The key strength of the design ...resides in its low complexity, which allows to obtain a low-cost and reliable hardware implementation for the new detector. The voltage source output goes from 200 mV to 800 mV, while the current source output is adjustable between 50 mA and 1 A. Experimental results from the sources characterization campaign validate the design: 24-hours time stability is demonstrated to be about 20 ppm for the voltage source, and below 1000 ppm for the minimum current of 50 mA of the current source. Furthermore, the measured superposed noise is less than −80 dBV and the Mean Time Between Failures (MTBF) is estimated to be about 20 million hours for both sources. The improvement of the quench detector features is also demonstrated through further experiments. The obtained results demonstrate the effectiveness of the proposed detection mechanism and pave the way for further research on large-scale use of high-temperature superconducting cables.
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for ...advanced analysis of colliders data. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments.
A method for a first-order approximation estimation of the longitudinal impedance of a synchrotron component, starting from power loss measurements on the device, is proposed. This method also ...estimates the resonance frequency and the quality factor of the impedance after the execution of several machine runs, without disconnecting the device. After a detailed description of the method, its suitability is demonstrated through a practical case study using power loss measurements of the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN).
•Hidden Markov Models are employed to build a fault detection model.•The model is trained by merely exploiting data from normal machine operation.•Multiple physical quantities are measured on the ...fluid machinery for the detection model building.•The model is validated by simulating a fault of the machine by properly corrupting data.
A fault detection method exploiting Hidden Markov Models (HMMs) is proposed for fluid machinery without adequate a priori information about faulty conditions. The method is trained only on data acquired during normal machine operation. For anomaly detection, typical quantities measured in monitoring fluid machines, namely 3-axis acceleration, electric power consumption, temperature, inlet and outlet pressure, are monitored. Principal Component Analysis is exploited for features extraction. Then, data is clustered and an HMM is trained. Finally, the trained model is employed together with a goodness-of-fit test to detect faulty states by processing online data. The method was tested and validated at CERN on screw compressors for cryogenic cooling.
•A PCB sensing coil for measuring the field of small-gap (<10 mm) accelerator magnets.•The sensor compensates the main field for increasing accuracy.•The design minimizes the impact of coil finite ...thickness.•Design criteria and fabrication methods are discussed.•Experimental results highlight significant accuracy improvement.
A miniaturized Printed Circuit Board (PCB) sensing coil, jointly developed by CERN and Fermilab for measuring the field of small-gap (less than 10 mm) accelerator magnets, is illustrated. A sensing coil array, with a scheme for compensating the main field when measuring the harmonic error components, hosted on a synthetic sapphire-based transducer, is presented. Key innovating features are (i) very-small size, both for the sensing coil array (thickness of 1.380 mm) and for the transducer (overall diameter of 7.350 mm), (ii) metrological performance, namely accuracy (more than five times better than state of the art), and 1-sigma repeatability (ten times better on harmonics with amplitude less than 100 ppm), and (iii) manufacturing technology of both the coil array (13 double layers aligned within 10 μm), and the sapphire support (concentricity, the most important uncertainty source for rotating coils, 3 μm of uncertainty, namely one order of magnitude better than fiberglass support). After stating the measurement problem, the design of the transducer and a case study of a two-layer PCB sensor array are illustrated. Then, the prototyping and quality control of both the sensor and the transducer are discussed. Furthermore, the calibration and the results obtained with a prototype setup at Fermilab are presented. Finally, in the appendix, the theory of the rotating coil, the sensor geometry, and the harmonic compensation are briefly reviewed for the reader easiness.
The crystal-based primary collimators installed in the Large Hadron Collider (LHC) at CERN use the channelling process in bent crystals to steer halo particles efficiently onto downstream ...collimators. This scheme, called crystal collimation, is also considered for applications of fixed-target implementations in the context of the Physics Beyond Collider at the LHC. Crystal collimation uses 4 mm-long silicon crystals that need to be approached very close to the high-intensity circulating beams, posing obvious concerns for machine impedance. A complex mechanical assembly was developed for this purpose. The setup includes also a system to control with sub-μrad accuracy the angular orientation of the crystal, which is done with a high-precision interferometric system. In order to prevent possible beam-induced instabilities and/or damage of the device components from excessive RF-heating, the electromagnetic (EM) characterization of this device is essential prior to its usage with high-intensity beams. In this article, the longitudinal impedance of the crystal primary collimator is studied extensively and estimations of power loss inside the device are provided for the different beam types planned at the LHC and at its High-Luminosity upgrade (HL-LHC). Electromagnetic simulations are performed on a realistic model that includes all the relevant components. The model is described in detail and computational challenges coming from its complexity are discussed. Care is taken to characterize the materials of each relevant sub-component. In particular, the lossy properties of silicon, whose complex permittivity is also evaluated through RF rectangular-cavity perturbation measurements, are taken into account. Numerical results are then compared with dedicated RF measurements performed on a prototype built for the LHC.
The construction of the superconducting magnets for the High-Luminosity upgrade of the Large Hadron Collider accelerator at Conseil Européen pour la Recherche Nucléaire has resulted in demands for ...1800 tons of ferromagnetic laminations for the magnet iron yokes. The magnetic properties of the selected steel, ARMCO Pure Iron, have been measured, within annealing treatment sequences from 750 °C to 850 °C, at operation temperatures of 4-300 K. It is shown that annealing treatment improves the magnetic performance of the material if the material is kept at room temperature. However, this improvement disappears after cooling down the material to the operation conditions of 4 K. The dependence on the operation temperature was shown by testing the material at the cryogenic temperature of roughly 4 K, 77 K, and at room temperature. The magnetic performance of the material shown at room temperature has not been found at cryogenic temperatures. Nevertheless, the results obtained are still acceptable in comparison to the properties of the alternative materials. Tests performed before and after the application of a mechanical stress have also been studied to validate the production process.
•Broadband HOM couplers have been proposed to damp resonances in cavities.•Proposed HOM couplers allow damping multiple resonances.•27 HOMs with different length, number of turns and diameter were ...tested.•Reduction of the resonance was observed both in simulation and experimental tests.
In particle accelerator components, parasitic resonances must be reduced because they heat up the equipment and cause beam instabilities. In this paper, a method for designing and characterizing Higher Order Mode (HOM) couplers for reducing such resonances in a broad bandwidth is proposed. A case study is considered for a specific component, called QuattroTank, showing geometrical discontinuities and thus causing significant electro-magnetic resonances. Results of numerical simulation and experimental emulation prove the capability of the proposed method to reduce the peaks and the Q-factor of the resonances.
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