Author: Tan, Y.E.
Paper Title Page
MOPPR001 Resonant Spin Depolarisation Measurements at the SPEAR3 Electron Storage Ring 771
  • K.P. Wootton, R.P. Rassool
    The University of Melbourne, Melbourne, Australia
  • M.J. Boland, Y.E. Tan
    ASCo, Clayton, Victoria, Australia
  • W.J. Corbett, M.H. Donald, X. Huang, R.R. Ortiz, J.A. Safranek, K. Tian
    SLAC, Menlo Park, California, USA
  Accurate electron beam energy measurements are valuable for precision lattice modelling of high-brightness light sources. At SPEAR3 the beam energy was measured using the resonant spin depolarisation method with striplines to resonantly excite the spin tune and a sensitive NaI scintillator beam loss monitor was used to detect resulting changes in Touschek lifetime. Using the combined apparatus an electron beam energy of 2.997251(7) GeV was measured, giving a relative uncertainty better than 3x10-6. The measured momentum compaction factor was found to be in close agreement with the numerical model value using rectangular defocussing gradient dipoles with measured magnetic field map profiles. In this paper we outline the chosen experimental technique, with emphasis on its applicability to electron storage rings in general.  
WEPPP057 Orbit Correction Studies using Neural Networks 2837
  • E. Meier, G. LeBlanc, Y.E. Tan
    ASCo, Clayton, Victoria, Australia
  This paper reports the use of Neural Networks for orbit correction at the Australian Synchrotron Storage Ring. The proposed system uses two Neural Networks in an actor-critic scheme to model a long term cost function and compute appropriate corrections. The system is entirely based on the history of the beam position and the actuators, the corrector magnets, in the storage ring. This makes the system auto-tuneable, which has the advantage of avoiding the use of a response matrix. As a generic and robust orbit correction program it can be used during commissioning and in slow orbit feedback. In this study, we present positive initial results of the simulations of the storage ring in Matlab. We will also discuss the possibility of reconstructing the response matrix from the information stored in the neural network for offline orbit response matrix analysis.