MétaCan
Menu
Back to cohort
Record W2102069251 · doi:10.1109/pac.1995.505803

Simulations of sawtooth instability

2002· article· en· W2102069251 on OpenAlexaff
R. Baartman, M. D'Yachkov

Bibliographic record

VenueProceedings Particle Accelerator Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsTRIUMF
Fundersnot available
KeywordsPhysicsSynchrotron radiationSawtooth waveInstabilityThermal emittanceBunchesOscillation (cell signaling)SynchrotronElectronWakePlasmoidAtomic physicsComputational physicsOpticsMechanicsBeam (structure)Nuclear physicsPlasmaMagnetic reconnection

Abstract

fetched live from OpenAlex

The equilibrium self-consistent distribution of particles in a high intensity electron synchrotron can be found using the Haissinski equation and the wake field. At some threshold intensity the bunch becomes unstable. However, radiation damping causes the particles to be confined and the instability does not necessarily cause loss of particles. It was observed in simulations with a very simple wake field and short bunches, that energy spread and bunch length oscillate in a sawtooth fashion. We find that this is due to the double-peaked nature of the stationary distribution. Over many synchrotron oscillations, particles diffuse from the head peak to the tail to the point where the tail peak becomes as large as the head. The two resulting sub-bunches then collapse together in less than one synchrotron oscillation, causing a net blow-up in emittance. Radiation damping reduces the emittance and diffusion begins again.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.043
GPT teacher head0.227
Teacher spread0.184 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations9
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueProceedings Particle Accelerator ConferenceSame topicParticle Accelerators and Free-Electron LasersFrench-language works237,207