A New Algorithm Improves our Understanding of Particle Beam Experimentation
The algorithm uses machine-learning techniques in combination with classical beam physics equations to reduce the amount of data processing.
When the linear acceleration at SLAC National Accelerator Laboratory operates, groups of about one billion electrons move through metal pipes almost at the speed of the light. The accelerator’s beam is formed by these electron groups, which are used to study the atomic behavior and innovative materials of molecules.
It is difficult to determine the exact appearance of a beam of particles as they move through an accelerator, so scientists can only make a rough estimation of the behavior of the beam during an experiment.
Source:
The Future of Particle Beam Experimentation – Innovative New Algorithm Improves Our Understanding
