IPAC'23 - Student Poster Session Guide

IPAC’23 / STUDENT POSTER SESSION GUIDE 44 Student Poster Session the beam diagnostic system structure will be much more concise and stable. In this paper, a developed direct RF sampling processor for beam diagnostic in SSRF will be introduced, and the first application on cavity BPM system will be shown. SUPM082 Stability analysis of double-harmonic cavity system in heavy beam loading with its feedback loops by a mathematical method based on Pedersen model Yubing Shen , Qiang Gu, Zihan Zhu (Shanghai Institute of Applied Physics), Duan Gu, Zenggong Jiang (Shanghai Advanced Research Institute). With the high beam current in storage ring, it is necessary to consider the instability problem caused by the heavy beam loading effect. It has been demonstrated that direct RF feedback (DRFB), autolevel control loop (ALC) and phase-lock loop (PLL) in the main cavity can lessen the impact of the beam effect. This paper regarded the beam, main cavity, harmonic cavity and feedback loops as double harmonic cavity system, and extended the transfer functions in the Pedersen model to this system. Some quantitative evaluations of simulation results have been got and conclusions have been drawn. In the case of a passive harmonic cavity, the optimiza- tion strategy of the controller parameters in the pre-detuning , ALC and PLL, as well as the gain and phase shift of DRFB were discussed. Meanwhile, it also involved the impact of the harmonic cavity feedback loop on the system stability at the optimum stretching condition when an active harmonic cavity was present. The research results can be used as guidelines for beam opera- tion with beam current increasing in the future. SUPM083 Identification of Magnetic Field Errors in Circular Accelerators based on Deep Lie Map Networks Conrad Caliari (Technische Universitaet Darmstadt) . Adrian Oeftiger (GSI Helmholtzzentrum für Schwerionenforschung GmbH), Oliver Boine-Frankenheim (Technische Universitaet Darmstadt). Magnetic field errors pose a limitation in the performance of circular accelerators, as they excite non-systematic resonances, reduce dynamic aperture and may result in beam loss. Their effect can be compensated assuming knowledge of their location and strength. Procedures based on orbit response matrices or resonance driving terms build a field error model sequentially for different accelerator sections, whereas a method detecting field errors in parallel yields the potential to save valuable beamtime. We introduce deep Lie map networks, which enable construction of an accelerator model including multipole components for the magnetic field er- rors by linking charged particle dynamics with machine learning methodology in a data-driven approach. Based on simulated beam-position- monitor readings for the example case of SIS18 at GSI, we demonstrate inference of location and strengths of quadrupole and sextupole errors for all accelerator sections in parallel. The obtained refined accelerator model may support set up of corrector magnets in operations to allow precise control over tunes, chromaticities and resonance compensation.