Dust-induced beam losses in the cryogenic arcs of the CERN Large Hadron Collider
Bibliographic record
Abstract
The interaction of dust particles with the LHC proton beams accounts for a major fraction of irregular beam loss events observed in LHC physics operation.The events cease after a few beam revolutions because of the expulsion of dust particles from the beam once they become ionized in the transverse beam tails.Despite the transient nature of these events, the resulting beam losses can trigger beam aborts or provoke quenches of superconducting magnets.In this paper, we study the characteristics of beam-dust particle interactions in the cryogenic arcs by reconstructing key observables like nuclear collision rates, loss durations and integral losses per event.The study is based on events recorded during 6.5 TeVoperation with stored beam intensities of up to ∼3 × 10 14 protons per beam.We show that inelastic collision rates can reach almost 10 12 collisions per second, resulting in a loss of up to ∼1.6 × 10 8 protons per event.We demonstrate that the experimental distributions and their dependence on beam parameters can be described quantitatively by a previously developed simulation model if dust particles are assumed to be attracted by the beam.The latter finding is consistent with recent time profile studies and yields further evidence that dust particles carry a negative charge when entering the beam.We also develop different hypotheses regarding the absence of higher-loss events in the measurements, although such events are theoretically not excluded by the simulation model.The results provide grounds for predicting dust-induced beam losses in the presence of higher-intensity beams in future runs of the High-Luminosity LHC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".