Online Data Monitoring of the ATLAS Muon System and Commissioning of the New Small Wheel (NSW) Data Quality System
Bibliographic record
Abstract
In order to efficiently handle the increased luminosity that will be provided by the High-Luminosity LHC (HL-LHC), the ATLAS Muon System was upgraded by replacing its first end-cap station (Small Wheel system) with a New Small Wheel (NSW) detector. The NSW detector provides high-precision muon track reconstruction, as well as information to the ATLAS Level-1 (L1) trigger for data recording. The data collected by the NSW along with other subsystems must be scrutinized to ensure the integrity of the detector, before making it available as "certified data" for “Physics Analyses”. This is achieved through the monitoring of detector-level quantities and reconstructed collision event characteristics at key stages of the data processing chain, using several Data Quality (DQ) tools. This paper, therefore, summarizes the development of the NSW DQ system and presents preliminary DQ monitoring results obtained from the early detector operation during the preparation of the Run3.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".