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Record W1986731305 · doi:10.1088/1748-0221/3/02/p02010

The ATLAS Forward Calorimeter

2008· article· en· W1986731305 on OpenAlexaff
A. Artamonov, D. Bailey, G Belanger, M. Cadabeschi, T -Y Chen, Vladimir Epshteyn, P. A. Gorbounov, K. K. Joo, M. Khakzad, V. Khovanskiy, P. Krieger, J. Mayer, E. Neuheimer, F. G. Oakham, M. O'Neill, R. S. Orr, M. Qi, J. P. Rutherfoord, Alexander Savin, M. Schram, P. B. Shatalov, L. Shaver, M. Shupe, G.G. Stairs, V. Strickland, D. Tompkins, I Tsukerman, K. Vincent

Bibliographic record

VenueJournal of Instrumentation · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsCarleton UniversityUniversity of Toronto
FundersU.S. Department of EnergyNational Science Foundation
KeywordsAtlas (anatomy)Calorimeter (particle physics)Nuclear physicsComputer sciencePhysicsOpticsGeology

Abstract

fetched live from OpenAlex

Forward calorimeters, located near the incident beams, complete the nearly 4π coverage for high p T particles resulting from proton-proton collisions in the ATLAS detector at the Large Hadron Collider at CERN. Both the technology and the deployment of the forward calorimeters in ATLAS are novel. The liquid argon rod/tube electrode structure for the forward calorimeters was invented specifically for applications in high rate environments. The placement of the forward calorimeters adjacent to the other calorimeters relatively close to the interaction point provides several advantages including nearly seamless calorimetry and natural shielding for the muon system. The forward calorimeter performance requirements are driven by events with missing E T and tagging jets.

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 score0.066
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0660.033

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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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

Citations49
Published2008
Admission routes1
Has abstractyes

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