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Record W2749576796 · doi:10.22323/1.287.0020

The ATLAS tracker strip detector for HL-LHC

2017· article· en· W2749576796 on OpenAlexaff
Kyle James Read Cormier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAtlas (anatomy)Large Hadron ColliderDetectorPhysicsAtlas detectorATLAS experimentCompact Muon SolenoidElectronicsElectrical engineeringNuclear physicsOpticsEngineering

Abstract

fetched live from OpenAlex

As part of the ATLAS upgrades for the High-Luminosity LHC (HL-LHC) the current ATLAS Inner Detector (ID) will be replaced by a new Inner Tracker (ITk). The ITk will consist of two main components: a semi-conductor pixel detector at the innermost radii, and a silicon strip detector covering larger radii out as far as the ATLAS solenoid magnet including the volume currently occupied by the ATLAS Transition Radiation Tracker (TRT). The primary challenges faced by the ITk are the higher planned read out rate of ATLAS, the higher density of charged particles in HL-LHC conditions, and the corresponding high radiation doses that the detector and electronics will receive. The ATLAS collaboration is currently working on designing and testing all aspects of the sensors, readout, mechanics, cooling and integration to meet these goals and a technical design report (TDR) is being prepared. This report is an overview of the ITk strip detector, highlighting the current status and the road ahead.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.016

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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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