Online Data Monitoring of the ATLAS Muon System and Commissioning of the New Small Wheel (NSW) Data Quality System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it