Paper  Title  Page 

MOA4I2 
Spacecharge Limits and Possible Mitigation Approaches in the FAIR Synchrotrons  


To fully exploit the potential of the new Facility for Antiproton and Ion Research (FAIR), the key synchrotrons SIS18 and SIS100 should be operated at the "space charge limit" for light and heavyion beams at a tolerable low beam loss of a few percent per cycle. A detailed 3D tracking model with collective effects (space charge and impedance) has been established including a realistic magnet field error model and the Landau Damping octupoles. The error model for SIS100 is based on precise bench measurements of the main magnets, the one for SIS18 on a novel datadriven beambased approach named Deep Lie Map Network. Simulations of the full onesecond SIS100 accumulation plateau determine the maximum achievable bunch intensity and the corresponding lowloss working point region. Several mitigation approaches have been scrutinised for their impact on the space charge limit: betabeat correction to suppress the halfinteger resonance, bunch flattening via double harmonic RF, and pulsed electron lenses (elenses). An optimum configuration for pulsed elens operation has been determined, options for additional coherent stabilisation as a Landau damping elens are currently studied.  
Slides MOA4I2 [3.697 MB]  
Cite •  reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
WEA1C2 
Design of a ProofofPrinciple Experiment for the DLMN Method to Identify Magnetic Field Errors  


Magnetic field errors limit the beam intensity in synchrotrons as they excite nonsystematic resonances, reduce dynamic aperture, and may result in beam loss due to space charge induced resonance crossing. Methods to establish a field error model from beambased measurements are therefore a valuable tool for realistic limitation and improvement studies. We report on the implementation of a proofofprinciple experiment in the GSI synchrotron SIS18 to identify both linear and nonlinear field errors. The goal is to demonstrate the Deep Lie Map Network (DLMN) technique, a proposed datadriven approach based on (unstructured) turnbyturn BPM data. Established identification procedures in the literature are based on orbit or tune response matrices, or resonance driving terms. While they sequentially build a field error model for subsequent accelerator sections, the DLMN approach could save valuable beam time by detecting field errors in parallel. We underline the potential of the DLMN method via detailed simulation studies to infer gradient and sextupole errors. The outline of a proofofprinciple experiment is discussed upon first experimental experience.  
Slides WEA1C2 [1.439 MB]  
Cite •  reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  