The performance of the New Small Wheel of the ATLAS detector in the heart of LHC Run-3
Bibliographic record
Abstract
During the second long shutdown of the Large Hadron Collider (LHC) at CERN, the most important upgrade within the ATLAS experiment was the replacement of the two inner endcap stations of the Muon Spectrometer (MS), with the New Small Wheel (NSW). Consisting of two novel detector technologies, the small-strip Thin Gap Chambers (sTGC) and the resistive strips Micromegas (MM), the NSW is targeting the rejection of fake muons at the endcap region between pseudorapidity 1 . 3 < | η | < 2 . 4 . Furthermore, thanks to the excellent muon tracking and the improved triggering capability NSW contributes to the identification of muons coming from the interaction point with high precision. Following an extensive effort to finalise the commissioning of the new detectors, both technologies were integrated successfully into the ATLAS data acquisition, reconstruction and simulation, and in 2024 they were also fully integrated into the ATLAS trigger, offering a significant reduction of the ATLAS Level-1 trigger rate and further reducing the readout dead-time. Despite the demanding challenges and increased luminosity delivered by LHC, the ATLAS NSW completed important milestones and now demonstrates its readiness towards the end of the Run-3 data-taking period of LHC. These proceedings will present an overview of the advances made within 2024, followed by a brief report of the NSW performance in terms of tracking and triggering, using data recorded from proton–proton collisions at 13.6 TeV .
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".