38 citations · 85 across the 5 of their papers we have counts for
9 papers
Beam Measurements and Machine Learning at the CERN Large Hadron Collider
P. Arpaia, G. Azzopardi, F. Blanc +16
This paper presents a review of the recent Machine Learning activities carried out on beam measurements performed at the CERN Large Hadron Collider. This paper has been accepted fo…
Beam-based aperture measurements with movable collimator jaws as performance booster of the CERN Large Hadron Collider
N. Fuster-Martínez, R. W. Aßmann, R. Bruce +6
The beam aperture of a particle accelerator defines the clearance available for the circulating beams and is a parameter of paramount importance for the accelerator performance. At…
Machine learning for beam dynamics studies at the CERN Large Hadron Collider
P. Arpaia, G. Azzopardi, F. Blanc +17
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of th…
Linear Imperfections
Jörg Wenninger
This lecture gives an overview of the impacts on linear machine optics of machine imperfections due to incorrect field settings and misalignments. The effects of imperfections in d…
Polarization and Centre-of-mass Energy Calibration at FCC-ee
Alain Blondel, Patrick Janot, Jörg Wenninger +25
The first stage of the FCC (Future Circular Collider) is a high-luminosity electron-positron collider (FCC-ee) with centre-of-mass energy ranging from 88 to 365 GeV, to study with…
FCC-ee: Your Questions Answered
Alain Blondel, Patrick Janot, Niloufar Alipour Tehrani +29
This document answers in simple terms many FAQs about FCC-ee, including comparisons with other colliders. It complements the FCC-ee CDR and the FCC Physics CDR by addressing many q…