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Beam-Beam Resonance Widths in the HL-LHC, and Reduction by Phasing of Interaction Points

2022· article· en· W6963178310 on OpenAlexaff

Bibliographic record

VenueJACOW · 2022
Typearticle
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsTRIUMF
Fundersnot available
KeywordsResonance (particle physics)BetatronPhaserInteraction pointReduction (mathematics)Interaction modelLimitingQuadrupolePhase (matter)

Abstract

fetched live from OpenAlex

Beam-beam interactions are a limiting factor in the planned high luminosity (HL) upgrade to the Large Hadron Collider (HL-LHC). Over the two main interaction regions of the LHC, a particle experiences two head-on and over a hundred long-range beam-beam interactions which drive betatron resonances in the system. Each resonance line in the space of horizontal and vertical tunes has a finite (non-zero) lock-on width. If the particles tunes fall within this width, they will eventually lock on to the resonance and be driven to large amplitude. We show that it is possible to reduce the resonances widths of a given order by using specific values of the phase advance between interaction points. This paper presents the derivation of resonance width for the weak-strong beam-beam effect, as an extension of A.Chaos width formulae for magnetic sextupoles. (A Lie-algebraic approach is used to combine the effect of the individual beam-beam impulses.) The paper then studies the lock-on width arising from two interaction regions containing 140 beam-beam impulses, and shows the cancellation of specific resonances by relative phasing of interaction points in the HL-LHC lattice.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.218

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)0.0000.000

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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