Assessment of the performance of High-Luminosity LHC operational scenarios: integrated luminosity and effective pile-up density
Bibliographic record
Abstract
The High-Luminosity Large Hadron Collider (HL-LHC) experiments will operate at unprecedented levels of event pile-up from proton–proton collisions at 14 TeV centre-of-mass energy. In this paper, we study the performance of the baseline and a series of alternative scenarios in terms of the delivered integrated luminosity and its quality (pile-up density). A new figure-of-merit is introduced, the effective pile-up density, a concept that reflects the expected detector efficiency in the reconstruction of event vertices for a given operational scenario, acting as a link between the machine and experimental sides. Alternative scenarios have been proposed either to improve the baseline performance or to provide operational schemes in the case of particular limitations. Simulations of the evolution of their optimum fills with the latest set of parameters of the HL-LHC are performed with β*-levelling, and the results are discussed in terms of both the integrated luminosity and the effective pile-up density. The crab kissing scheme, a proposed scenario for pile-up density control, is re-evaluated under this new perspective with updated beam and optics parameters. Estimates on the expected integrated luminosity due to the impact of crab cavity noise, full crab crossing, and reduced cross section for burn-off, are also presented.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".