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Transverse excitation for beam diagnostics and slow extraction from synchrotrons
No full textTransverse excitation is a key method required for the operation of synchrotrons, a type of circular particle accelerator suitable for a wide range of applications. The excitation is essential to control the beam: First, it is used in the context of beam diagnostics to enable monitoring of the accelerator's working point (tune). This is required to setup the machine and to avoid unintentional beam losses. Second, it is used in the context of resonant slow extraction to drive and control the extraction of particles from the accelerator. This method referred to as Radio Frequency Knock Out (RF-KO) enables the delivery of defined beam intensities for experiments or medical treatments. Transverse excitation is performed by creating a time-dependent electromagnetic field through which particles are deflected on each subsequent turn in the synchrotron. To generate this dipolar field with frequencies in the radio frequency (RF) domain, signal generators, amplifiers and stripline kickers (exciters) are utilized.This thesis comprises a detailed study of the method of transverse excitation. Special focus is placed on the nonlinear beam dynamics, the composition and generation of the excitation signal and the peripheral systems (detectors, exciters). An excitation system for tune diagnostics and one for resonant slow extraction is developed and used to study different methods for transverse excitation experimentally. Particle tracking simulations are carried out to gain a detailed understanding of the excitation process. Based on the findings from experiments and simulations, recommendations are given for the improved application of excitation techniques. Two new excitation methods for resonant slow extraction are developed, studied and compared to other commonly applied methods. The sensible application of these excitation techniques is essential to improve the quality of the particle beam and the operation and performance of the synchrotron
Investigation of thermal and structural integrity of modules and ladders of Silicon Tracking System of the CBM experiment
No full textThe Compressed Baryonic Matter (CBM) at the Facility for Antiproton and Ion Research (FAIR) is a fixed target experiment designed to investigate the properties of strongly interacting matter in the region of high net-baryon density. The Silicon Tracking System (STS) is the core detector of the CBM experiment and aims to track and measure the momentum of the charged particles. The STS detector comprises of 876 double sided silicon micro-strip sensors connected via micro cables to the Front-End Boards (FEBs) which are kept outside the detector acceptance of 2.5° to 25°. These sensors are mounted on 106 carbon fiber ladders which includes standard ladders and central ladders with an opening for the beam-pipe. For good particle tracking accuracy in the CBM, the silicon sensors must be mounted on the ladders with extremely high precision, minimizing misalignment and optimizing the spatial resolution of the detector. The experimental operating conditions of STS present challenges to the electronics due to a highly variable thermal environment. A significant portion of the thesis focuses on the thermal studies of the STS components. This involves a detailed investigation of the requirements for thermal interface materials (TIMs) between the FEBs and the cooling shelves. The study includes optimization techniques for adhesive application and thermal testing to ensure the effectiveness of the TIMs. To ensure the reliable functioning of FEBs under significant temperature variations, thermal cycling tests were conducted, and potential failure scenarios have been analyzed. The main focus of the thesis is the understanding of the structural integrity of the STS detector. It is investigated how the STS ladders, essential for supporting the silicon sensors, are put together and how they perform. The design and quality assurance processes for carbon fiber ladders are examined, followed by a step-by-step description of the ladder assembly procedure. The evolution of the ladder assembly procedures, from initial prototypes to fully functional ladders with the required mounting precision are highlighted. The developed procedure is designed to be iterative and easily adaptable for producing 106 STS ladders. The final section of the thesis addresses the vibration challenges encountered by the STS ladders due to air cooling, which is essential for maintaining the sensor performance. It describes the experimental setups used to measure the eigenfrequencies and vibrations on the sensor surface under airflow conditions. The study uses a perforated tube to direct airflow onto the sensor surfaces and highlights the performance differences between the standard and central ladders. Through the analysis of vibration magnitude, the impact of airflow on the stability of the silicon sensors once they are mounted on the ladders, is evaluated. These findings underline the significance of effective vibration control to maintain sensor stability. This thesis provides a comprehensive understanding of both thermal management and structural integrity of the STS. Through extensive testing of TIM and thermal cycling of the FEBs, the last step of the module assembly process has been optimized, resulting in a reliable TIM now used in the series production of the modules. Along this work, significant progress has been made in developing the ladder assembly procedure, which is now being implemented for all the ladders, with series production already underway. The central ladder assembly procedure has been optimized and validated with a prototype ladder. The vibration measurements have established the boundary conditions for airflow through the perforated tube, ensuring the mechanical integrity and necessary cooling to prevent thermal runaway
On-Shot Detection of Fission Isotopes of 238U, Produced by Laser-Driven X-Rays
No full textThis dataset was collected during experiment P-21-00005 conducted at the PHELIX Laser Facility at GSI Helmholtzzentrum für Schwerionenforschung GmbH. This complete dataset supports the findings from the paper and is structured in the following way: - calibration_source.pdf /// Contains details about the calibration source used to support detector calibration. - detector_calibration.xlsx /// Provides all relevant data for calibrating the detector in terms of energy and efficiency. - photon_spectrum.xlsx /// Includes information on the generated bremsstrahlung photon spectrum. - README.txt /// Describes the structure and interpretation of the data_p2023_shot_{} files. - data_p2023_shot_{} /// A series of files containing measured event data from high-purity germanium detectors, including the decay spectra of the produced isotopes
A generative adversarial network to improve integrated mode proton imaging resolution using paired proton–carbon data
No full textIntegrated mode proton imaging is a clinically accessible method for proton radiographs (pRads), but its spatial resolution is limited by multiple Coulomb scattering (MCS). As the amplitude of MCS decreases with increasing particle charge, heavier ions such as carbon ions produce radiographs with better resolution (cRads). Improving image resolution of pRads may thus be achieved by transferring individual proton pencil beam images to the equivalent carbon ion data using a trained image translation network. The approach can be interpreted as applying a data-driven deconvolution operation with a spatially variant point spread function.Propose a deep learning framework based on paired proton-carbon data to increase the resolution of integrated mode pRads.A conditional generative adversarial network, Proton2Carbon, was developed to translate proton pencil beam images into synthetic carbon ion beam images. The model was trained on 547 224 paired proton-carbon images acquired with a scintillation detector at the Marburg Ion Therapy Centre. Image reconstruction was performed using a 2D lateral method, and the model was evaluated on internal and external datasets for spatial resolution, using custom 3D-printed line pair modules.The Proton2Carbon model improved the spatial resolution of pRads from 1.7 to 2.7 lp/cm on internal data and to 2.3 lp/cm on external data, demonstrating generalizability. Water equivalent thickness accuracy remained consistent with pRads and cRads. Evaluation on an anthropomorphic head phantom showed enhanced structural clarity, though some increased noise was observed.This study demonstrates that deep learning can enhance pRad image quality by leveraging paired proton-carbon data. Proton2Carbon can be integrated into existing imaging workflows to improve clinical and research applications of proton radiography. To facilitate further research, the full dataset used to train Proton2Carbon is publicly released and available at https://zenodo.org/records/14945165
