Integral Resonance Control In Continuous Wave Superconducting Particle Accelerators
Sign Up to like & getrecommendations! Published in 2020 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2020.12.186
Abstract: Abstract Superconducting accelerating cavities for continuous wave low-current particle accelerators requires a tight resonance control to optimize the RF power costs and to minimize the beam delivery downtime. When the detuning produced by radiation pressure… read more here.
Keywords: resonance control; particle accelerators; control; resonance ... See more keywords
Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning
Sign Up to like & getrecommendations! Published in 2021 at "Nature Communications"
DOI: 10.1038/s41467-021-25757-3
Abstract: Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based experiments. Currently used techniques… read more here.
Keywords: space; exploration; input; turn key ... See more keywords
Cascaded particle accelerators reach new energy
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DOI: 10.1038/s41566-021-00822-x
Abstract: The successful demonstration of two-stage acceleration driven by terahertz pulses bodes well for the future development of compact, efficient particle accelerators. read more here.
Keywords: particle accelerators; new energy; accelerators reach; reach new ... See more keywords
Particle Accelerators as Tools to Investigate Astrochemistry
Sign Up to like & getrecommendations! Published in 2017 at "Nuclear Physics News"
DOI: 10.1080/10619127.2016.1246018
Abstract: Around the 1970s the use of Van de Graaff type ion accelerators (mostly having voltages between 10 s of kV and 1–2 MV) became inadequate for nuclear physics that required higher and higher energies to… read more here.
Keywords: accelerators tools; investigate astrochemistry; physics; ion ... See more keywords
Machine learning-based longitudinal phase space prediction of particle accelerators
Sign Up to like & getrecommendations! Published in 2018 at "Physical Review Accelerators and Beams"
DOI: 10.1103/physrevaccelbeams.21.112802
Abstract: We report on the application of machine learning (ML) methods for predicting the longitudinal phase space (LPS) distribution of particle accelerators. Our approach consists of training a ML-based virtual diagnostic to predict the LPS using… read more here.
Keywords: machine learning; longitudinal phase; phase space; particle ... See more keywords