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HADES is a fixed target experiment using SIS18 heavy-ion beams. It investigates the microscopic properties of matter formed in heavy-ion, proton and pion - induced reactions in the 1-3.5 GeV/u energy regime. In 2014 HADES used a secondary pion beam produced by interaction between high-intensity nitrogen primary beam and a beryllium target. In these conditions beam losses, generated by slow extraction and beam transport to the experimental area, led to activation of the beam line elements and triggered radiation alarms. The primary beam intensity had to be reduced and the beam optics modified in order to keep radiation levels within the allowed limits. Similar beam conditions are requested by HADES experiment for upcoming run in 2018 and in the following years. Therefore, a number of measures have been proposed to improve beam transmission and quality. These measures are: additional shielding, additional beam instrumentation, modification of beam optics and increase of vacuum chambers' apertures in critical locations. The optics study and preliminary results of FLUKA simulations for optimization of location of loss detectors are presented.
The present study focuses on the beam line optimization from the heavy-ion synchrotron SIS18 to the HADES experiment. BOBYQA (Bound Optimization BY Quadratic Approximation) solves bound constrained optimization problems without using derivatives of the objective function. The Bayesian optimization is another strategy for global optimization of costly, noisy functions without using derivatives. A python programming interface to MADX allow the use of the python implementation of BOBYQA and Bayesian method. This gave the possibility to use tracking simulation with MADX to determine the loss budget for each lattice setting during the optimization and compare both optimization methods.
Due to the massive parallel operation modes at GSI accelerators, a lot of accelerator setup and re-adjustment has to be made by operators during a beam time. This is typically done manually using potentiometers and is very time-consuming. With the FAIR project the complexity of the accelerator facility increases further and for efficiency reasons it is recommended to establish a high level of automation for future operation. Modern Accelerator Control Systems allow a fast access to both, accelerator settings and beam diagnostics data. This provides the opportunity to implement algorithms for automated adjustment of e.g. magnet settings to maximize transmission and optimize required beam parameters. The fast-switching magnets in GSI-beamlines are an optimal basis for an automatic exploration of the parameter-space. The optimization of the parameters for the SIS18 multi-turn-injection using a genetic algorithm has already been simulated*. The first results of our automatized online parameter optimization at the CRYRING@ESR injector are presented here.
The first part of this work addresses the automatic online tuning of transfer lines in particle accelerator facilities. In the second part the focus lies on the automatic construction and optimisation of such transport lines. It can be shown that genetic algorithms can be used very well for optimisation in both cases. Automatic online tuning can be performed very efficiently at accelerators under certain boundary conditions and is particularly well suited for initial beam commissioning with low intensity pilot beams. The construction of transfer lines can also be formulated and solved as an minimisation problem with an adopted parameterisation. Thereby, both the imaging properties of the beam transport and the robustness against error studies can be optimised at the same time.
GSI High Energy Beam Transfer lines (HEST) link the SIS18 synchrotron with two storage rings (Experimental Storage Ring and Cryring) and six experimental caves. The recent upgrades to HEST beam instrumentation enables precise measurements of beam properties along the lines and allow for faster and more precise beams setup on targets. Preliminary results of some of the measurements performed during runs in 2018 and 2019 are presented here. The focus is on response matrix measurements and quadrupole scans performed on HADES beam line. The errors and future improvements are discussed.
Since the last 20 years, modern heuristic algorithms and machine learning have been increasingly used for several purposes in accelerator technology and physics. Since computing power has become less and less of a limiting factor, these tools have become part of the physicist community's standard toolkit [1][2] [3] [4] [5]. This paper describes the construction of an algorithm that can be used to generate an optimised lattice design for transfer lines under the consideration of restrictions that usually limit design options in reality. The developed algorithm has been applied to the existing SIS18 to HADES transfer line in GSI.
Challenges of FAIR phase 0
(2018)
After two-year's shutdown, the GSI accelerators plus the latest addition of storage ring CRYRING, will be back into operation in 2018 as the FAIR phase 0 with the goal to fulfill the needs of scientific community and the FAIR accelerators and detector development. Even though GSI has been well known for its operation of a variety of ion beams ranging from proton up to uranium for multi research areas such as nuclear physics, astrophysics, biophysics, material science, the upcoming beam time faces a number of challenges in re-commissioning its existing circular accelerators with brand new control system and upgrade of beam instrumentations, as well as in rising failures of dated components and systems. The cycling synchrotron SIS18 has been undergoing a set of upgrade measures for fulfilling future FAIR operation, among which many measures will also be commissioned during the upcoming beam time. This paper presents the highlights of the challenges such as re-establishing the high intensity heavy ion operation as well as parallel operation mode for serving multi users. The status of preparation including commissioning results will also be reported.
An automated beam-setting optimization application has been implemented on top of FAIR’s control system software stack based on CERN’s LSA framework. The optimization functionality is built using the Jenetics software library implemented in Java. Tests of the software with beam have been performed at the CRYRING@ESR ion storage ring.
The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
