OOIR: Observatory of International Research

Papers

(The H4-Index of Physical Review Accelerators and Beams is 16. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-11-01 to 2024-11-01.)

Article	Citations
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory	42
Beam dynamics corrections to the Run-1 measurement of the muon anomalous magnetic moment at Fermilab	33
Commissioning of the hybrid multibend achromat lattice at the European Synchrotron Radiation Facility	30
Staging of plasma-wakefield accelerators	28
Sample-efficient reinforcement learning for CERN accelerator control	27
Multiobjective Bayesian optimization for online accelerator tuning	26
Physics model-informed Gaussian process for online optimization of particle accelerators	23
Design of the third-generation lead-based neutron spallation target for the neutron time-of-flight facility at CERN	22
Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster	21
High-quality positron acceleration in beam-driven plasma accelerators	20
Demonstration of tailored energy deposition in a laser proton accelerator	19
Novel X -band transverse deflection structure with variable polarization	19
Compact S -band linear accelerator system for ultrafast, ultrahigh dose-rate radiotherapy	19
Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks	18
Design development and implementation of an irradiation station at the neutron time-of-flight facility at CERN	17
Achromatic beamline design for a laser-driven proton therapy accelerator	17
Enhanced seeded free electron laser performance with a “cold” electron beam	16
Successful user operation of a superconducting radio-frequency photoelectron gun with Mg cathodes	16
Magnetized particle transport in multi-MA accelerators	16
Uncertainty quantification for deep learning in particle accelerator applications	16