Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userLinear Accelerators
391
0
0.0
(
0
)
Added by
Scientific Information Service CERN
Publication date
2016
fields
Physics
and research's language is
English
Authors
M. Vretenar
Ask ChatGPT about the research
No Arabic abstract
The main features of radio-frequency linear accelerators are introduced, reviewing the different types of accelerating structures and presenting the main characteristics aspects of linac beam dynamics.
rate research
Read More
When a charged particle travels across the vacuum chamber of an accelerator, it induces electromagnetic fields, which are left mainly behind the generating particle. These electromagnetic fields act back on the beam and influence its motion. Such an interaction of the beam with its surro undings results in beam energy losses, alters the shape of the bunches, and shifts the betatron and synchrotron frequencies. At high beam current the fields can even lead to instabilities, thus limiting the performance of the accelerator in terms of beam quality and current intensity. We discuss in this lecture the general features of the electromagnetic fields, introducing the concepts of wakefields and giving a few simple examples in cylindrical geometry. We then show the effect of the wakefields on the dynamics of a beam in a linac, dealing in particular with the beam breakup instability and how to cure it.
We present an analysis of electromagnetic oscillations in a spherical conducting cavity filled concentrically with either dielectric or vacuum layers. The fields are given analytically, and the resonant frequency is determined numerically. An important special case of a spherical conducting cavity with a smaller dielectric sphere at its center is treated in more detail. By numerically integrating the equations of motion we demonstrate that the transverse electric oscillations in such cavity can be used to accelerate strongly relativistic electrons. The electrons trajectory is assumed to be nearly tangential to the dielectric sphere. We demonstrate that the interaction of such electrons with the oscillating magnetic field deflects their trajectory from a straight line only slightly. The Q factor of such a resonator only depends on losses in the dielectric. For existing ultra low loss dielectrics, Q can be three orders of magnitude better than obtained in existing cylindrical cavities.
This paper is a summary report of the ICFA Beam Dynamics Workshop Accelerators for a Higgs Factory: Linear vs. Circular (HF2012). It discusses four types of accelerators as possible candidates for a Higgs factory: linear e+e- colliders, circular e+e- colliders, muon collider and photon colliders. The comparison includes: physics reach, performance (energy and luminosity), upgrade potential, technology maturity and readiness, and technical challenges requiring further R&D.
Crystal lattice can trap and channel particle beams along major crystallographic directions. In a bent crystal, the channelled particles follow the bend. This makes a basis for an elegant technique of beam steering by means of bent channelling crystals, experimentally demonstrated from 3 MeV to 1 TeV. This technique was strongly developed in recent studies at CERN, FNAL, IHEP, and BNL, and can lead to interesting applications also at the LHC, such as crystal collimation making a collider cleaner by an order of magnitude. We review recent developments in the field and show outlook for the future.
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 rather than a priori models. ML techniques are now technologically mature enough to be applied to particle accelerators, and we expect that ML will become an increasingly valuable tool to meet new demands for beam energy, brightness, and stability. The intent of this white paper is to provide a high-level introduction to problems in accelerator science and operation where incorporating ML-based approaches may provide significant benefit. We review ML techniques currently being investigated at particle accelerator facilities, and we place specific emphasis on active research efforts and promising exploratory results. We also identify new applications and discuss their feasibility, along with the required data and infrastructure strategies. We conclude with a set of guidelines and recommendations for laboratory managers and administrators, emphasizing the logistical and technological requirements for successfully adopting this technology. This white paper also serves as a summary of the discussion from a recent workshop held at SLAC on ML for particle accelerators.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
