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 - Fol, E. AU - Rogers, C.T. AU - Schulte, D. 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 - Machine Learning-Based Modeling of Muon Beam Ionization Cooling J2 - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022 CY - Bangkok, Thailand T2 - International Particle Accelerator Conference T3 - 13 LA - english AB - Surrogate modeling can lead to significant improvements of beam dynamics simulations in terms of computational time and resources. Application of supervised machine learning, using collected simulation data allows to build surrogate models which can estimate beam parameters evolution based on the provided cooling channel design. The created models help to understand the correlations between different lattice components and the importance of specific beam properties for the cooling performance. We present the application of surrogate modeling to enhance final muon cooling design studies, demonstrating the potential of such approach to be integrated into the design and optimization of other components of future colliders. PB - JACoW Publishing CP - Geneva, Switzerland SP - 2354 EP - 2357 KW - emittance KW - simulation KW - target KW - lattice KW - collider DA - 2022/07 PY - 2022 SN - 2673-5490 SN - 978-3-95450-227-1 DO - doi:10.18429/JACoW-IPAC2022-WEPOMS046 UR - https://jacow.org/ipac2022/papers/wepoms046.pdf ER -