Coherent Electron Cooling Experiment at RHIC - Project Overview and Experimental Possibilities
Coherent electron cooling experiment at RHIC is led by Vladimir N. Litvinenko with Jean Clifford Brutus as the project manager. The CeC group, consisting of researchers from Brookhaven National Laboratory and Stony Brook University, aims to test various experimental scenarios, including high gain FE
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Electron Lenses in Particle Accelerators: Advancements and Applications
Explore the use of electron lenses in particle accelerators for space-charge compensation, Landau damping, halo collimation, and beam-beam compensation. Learn about their history, applications in Tevatron and RHIC colliders, and current research areas. Electron lenses have shown promise in enhancing
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Charm in Heavy Ion Collisions: Experimental Comparisons from SPS to LHC
Investigating charm suppression in heavy ion collisions at different energy levels, this study compares experimental data from SPS to LHC. Analysis suggests similar suppression at SPS and RHIC, with potential factors like recombination and direct J/ψ production influencing results. Uncertainties re
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Heavy Ion Physics at RHIC: RUN14-16 Overview
Exploring heavy ion physics at RHIC through the RUN14-16 campaigns, focusing on measuring heavy quarks like charm and bottom, the experimental setups, beam energies, and the significance of probing Quark Gluon Plasma using heavy quarks. Detailed information about the runs, detectors used, and the ma
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High Intensity in the Injectors: Achievements and Innovations
Booster ring realigned, AGS octupoles revived, stopband correction developed. Focus on compensating loss, species intensities, AGS bunch merge, and enhancements for RHIC. Challenges in Ru isotope separation and achieving desired intensities discussed.
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Summary and Updates from RHIC Machine Experiments Meeting
General discussion and updates from the Run 19 RHIC Machine Experiments Meeting held on January 7, 2020, including discussions on Run 20, Fixed Target Running for STAR, LEReC status updates, and other business matters. Key points covered include the schedule assumptions, interleaving LEReC commissio
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Machine Learning Applications for EBIS Beam Intensity and RHIC Luminosity Maximization
This presentation discusses the application of machine learning for optimizing EBIS beam intensity and RHIC luminosity. It covers topics such as motivation, EBIS beam intensity optimization, luminosity optimization, and outlines the plan and summary of the project. Collaborators from MSU, LBNL, and
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