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Record W6969084473 · doi:10.5281/zenodo.5018302

XLS - D3.2: Review report on bunch compression techniques and phase space linearization

2019· article· en· W6969084473 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsCanadian Nautical Research Society
FundersEuropean Commission
KeywordsCompression (physics)Basis (linear algebra)Linear particle acceleratorBeam (structure)LinearizationCathode rayPhase (matter)Tracking (education)Particle accelerator

Abstract

fetched live from OpenAlex

This document describes with analytical and numerical methods, i.e., approximated mathematical expressions and particle tracking runs, the process of bunch length compression of relativistic electron beams in linear accelerators. In particular, it reviews state-of-the-art compression options, based on magnetic insertions and radio-frequency (RF) accelerators, for the increase of peak current and preservation of the six-dimensional beam brightness as requested by x-ray freeelectron lasers (FELs). After a theoretical introduction to the topic, guidelines for the determination of the compression scheme for the CompactLight FEL are provided, by considering for example RF compression in combination with four dipoles chicanes. The study considers single particle dynamics, tolerance budget and collective effects involved in the compression process. On the basis of FEL specifications, the main parameters of the electron beam and of the compressors in the CompactLight accelerator are illustrated. Finally, a preliminary RF design of X-band and Ka-band accelerating cavities to support magnetic compression is presented. As a result, this review of most advanced electron beam compression schemes provides a solid basis for the definition of the beam manipulation aimed to meet the requirements of CompactLight FEL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.262
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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