Paper |
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TUAM1HA01 |
Progress of the Stochastic Cooling System of the Collector Ring |
40 |
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- C. Dimopoulou, D. Barker, R.M. Böhm, A. Dolinskyy, B. J. Franzke, R. Hettrich, W. Maier, R. Menges, F. Nolden, C. Peschke, P. Petri, M. Steck
GSI, Darmstadt, Germany
- L. Thorndahl
CERN, Geneva, Switzerland
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An overview of the recent achievements and ongoing developments for the stochastic cooling system of the Collector Ring is given. In focus are the hardware developments as well as the progress in predicting the system performance. The system operates in the frequency band 1-2 GHz, it has to provide fast 3D cooling of antiproton, rare isotope and stable heavy ion beams. The main challenges are (i) the cooling of antiprotons by means of cryogenic movable pick-up electrodes and (ii) the fast two-stage cooling (pre-cooling by the Palmer method, followed by the notch filter method) of the hot rare isotopes. Recently, a novel code for simulating the cooling process in the time domain has been developed at CERN. First results for the momentum cooling for heavy ions in the CR will be shown in comparison with results obtained in the frequency domain with the Fokker-Planck equation.
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Slides TUAM1HA01 [4.320 MB]
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WEPPO21 |
Design of the Palmer Pickup for Stochastic Pre-cooling of Heavy Ions at the CR |
149 |
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- L. Thorndahl
CERN, Geneva, Switzerland
- D. Barker, C. Dimopoulou, C. Peschke
GSI, Darmstadt, Germany
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We detail the design and optimisation of modified Faltin type pick-ups for the pre-cooling of heavy ions in the CR ring at GSI. The design challenge includes simultaneous optimisation of large pick-up impedance, 50 Ω characteristic impedance, frequency independence over a large bandwidths and phase velocity matched to particle velocity. Although heavy ions offer large signals due to their charge, their relatively slow velocity is difficult to match in Faltin type pick-ups while maintaining a flat frequency response and a 50 Ω characteristic impedance. We explain the design process, and show how multiple parameters are simultaneously optimised using genetic algorithms, which are suitable for optimization problems with large and complex search spaces.
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