LUCID-3: the upgrade of the ATLAS Luminosity detector for High Luminosity LHC
Bibliographic record
Abstract
LUCID-2 ($\textbf{LU}$minosity $\textbf{C}$herenkov $\textbf{I}$ntegrating $\textbf{D}$etector) has been the main luminometer for the ATLAS experiment during Run-2 (2015-2018) and Run-3 (2022-ongoing). Offline a precision on the $\mathcal{L}$ measurement for the entirety of Run-2 of 0.8% was achieved, and a similar precision is expected at the conclusion of Run-3. LUCID-2 however will not be able to produce competitive measurements of luminosity in the HL-LHC era, particularly due to the increased number of collisions per bunch crossing. Due to this, an upgrade to LUCID-2, LUCID-3, is under development. Multiple technologies and designs have been proposed, of which prototypes have been produced, installed and have been operating alongside LUCID-2. These prototypes and the results obtained with them are discussed in order to evaluate their potential effectiveness in the HL-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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".