JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
TY - CONF AU - Van der Veken, F.F. AU - Akbari, R. AU - Bogaert, M.P. AU - Fol, E. AU - Giovannozzi, M. AU - Lowyck, A.L. AU - Montanari, C.E. AU - Van Goethem, W. ED - Zimmermann, Frank ED - Tanaka, Hitoshi ED - Sudmuang, Porntip ED - Klysubun, Prapong ED - Sunwong, Prapaiwan ED - Chanwattana, Thakonwat ED - Petit-Jean-Genaz, Christine ED - Schaa, Volker R.W. TI - Determination of the Phase-Space Stability Border with Machine Learning Techniques J2 - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022 CY - Bangkok, Thailand T2 - International Particle Accelerator Conference T3 - 13 LA - english AB - The dynamic aperture (DA) of a hadron accelerator is represented by the volume in phase space that exhibits bounded motion, where we disregard any disconnected parts that could be due to stable islands. To estimate DA in numerical simulations, it is customary to sample a set of initial conditions using a polar grid in the transverse planes, featuring a limited number of angles and using evenly distributed radial amplitudes. This method becomes very CPU intensive when detailed scans in 4D, and even more in higher dimensions, are used to compute the dynamic aperture. In this paper, a new method is presented, in which the border of the phase-space stable region is identified using a machine learning (ML) model. This allows one to optimise the computational time by taking the complex geometry of the phase space into account, using adaptive sampling to increase the density of initial conditions along the border of stability. PB - JACoW Publishing CP - Geneva, Switzerland SP - 183 EP - 186 KW - dynamic-aperture KW - luminosity KW - hadron KW - storage-ring KW - simulation DA - 2022/07 PY - 2022 SN - 2673-5490 SN - 978-3-95450-227-1 DO - doi:10.18429/JACoW-IPAC2022-MOPOST047 UR - https://jacow.org/ipac2022/papers/mopost047.pdf ER -