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Record W4411507427 · doi:10.1016/j.nima.2025.170750

The performance of the New Small Wheel of the ATLAS detector in the heart of LHC Run-3

2025· article· en· W4411507427 on OpenAlexaff
S. Tsigaridas

Bibliographic record

VenueNuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsTRIUMF
Fundersnot available
KeywordsLarge Hadron ColliderAtlas (anatomy)DetectorAtlas detectorPhysicsParticle physicsMedicineAnatomyOptics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.344
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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