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@inproceedings{bruchon:icalepcs2023-tupdp086, author = {N. Bruchon and I. Karpov and N. Madysa and G. Papotti and D. Quartullo}, title = {{Operational Tool for Automatic Setup of Controlled Longitudinal Emittance Blow-Up in the CERN SPS}}, % booktitle = {Proc. ICALEPCS'23}, booktitle = {Proc. 19th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'23)}, eventdate = {2023-10-09/2023-10-13}, pages = {723--728}, paper = {TUPDP086}, language = {english}, keywords = {controls, emittance, operation, target, software}, venue = {Cape Town, South Africa}, series = {International Conference on Accelerator and Large Experimental Physics Control Systems}, number = {19}, publisher = {JACoW Publishing, Geneva, Switzerland}, month = {02}, year = {2024}, issn = {2226-0358}, isbn = {978-3-95450-238-7}, doi = {10.18429/JACoW-ICALEPCS2023-TUPDP086}, url = {https://jacow.org/icalepcs2023/papers/tupdp086.pdf}, abstract = {{The controlled longitudinal emittance blow-up is necessary to ensure the stability of high-intensity LHC-type beams in the CERN SPS. It consists of diffusing the particles in the bunch core by injecting a bandwidth-limited noise into the beam phase loop of the main 200 MHz RF system. Obtaining the correct amplitude and bandwidth of this noise signal is non-trivial, and it may be tedious and time-demanding if done manually. An automatic approach was developed to speed up the determination of optimal settings. The problem complexity is reduced by splitting the blow-up into multiple sub-intervals for which the noise parameters are optimized by observing the longitudinal profiles at the end of each sub-interval. The derived bunch lengths are used to determine the objective function which measures the error with respect to the requirements. The sub-intervals are tackled sequentially. The optimization moves to the next one only when the previous sub-interval is completed. The proposed tool is integrated into the CERN generic optimization framework that features pre-implemented optimization algorithms. Both single- and multi-bunch high-intensity beams are quickly and efficiently stabilized by the optimizer, used so far in high-intensity studies. A possible extension to Bayesian optimization is being investigated. }}, }