122 citations · 366 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
Opportunities in Machine Learning for Particle Accelerators
Auralee Edelen, Christopher Mayes, Daniel Bowring +10
Machine learning (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data…
Simulations and measurements of beam loss patterns at the CERN Large Hadron Collider
R. Bruce, R. W. Assmann, V. Boccone +21
The CERN Large Hadron Collider (LHC) is designed to collide proton beams of unprecedented energy, in order to extend the frontiers of high-energy particle physics. During the first…
Measurements of the effect of collisions on transverse beam halo diffusion in the Tevatron and in the LHC
G. Stancari, G. Annala, T. R. Johnson +9
Beam-beam forces and collision optics can strongly affect beam lifetime, dynamic aperture, and halo formation in particle colliders. Extensive analytical and numerical simulations…
Beam halo dynamics and control with hollow electron beams
G. Stancari, G. Annala, A. Didenko +15
Experimental measurements of beam halo diffusion dynamics with collimator scans are reviewed. The concept of halo control with a hollow electron beam collimator, its demonstration…