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Test beam measurement of ATLAS ITk Short Strip module at warm and cold operational temperature

2023· article· en· W4323660779 on OpenAlexaff
J.-H. Arling, C. Becot, E. Buchanan, J. Dopke, B. J. Gallop, J. Kaplon, J. S. Keller, J. Kroll, Yinsheng Li, Zhigang Li, Junwei Liu, Y Liu, Sam Yanwing Ng, R. Přívara, A. Renardi, A. Rodríguez Rodríguez, E. Rossi, Frederik Ruehr, C. Sawyer, D. Sperlich, A. R. Weidberg, D. F. Zhang

Bibliographic record

VenueJournal of Instrumentation · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsCarleton University
FundersDeutsches Elektronen-SynchrotronScience and Technology Facilities CouncilEuropean CommissionCERN
KeywordsDESYAtlas (anatomy)PhysicsNoise (video)Large Hadron ColliderOpticsNuclear physicsComputer science

Abstract

fetched live from OpenAlex

Abstract This study is focused on an investigation of the performance of the Short Strip module developed by the ATLAS Inner Tracker (ITk) strip collaboration using electron beams of energy 5.4 GeV and 5.8 GeV at the DESY-II Testbeam Facility. The noise at +30 °C and -30 °C was measured. The ratio of the two measurements is compared with a circuit-model calculation. The measured noise at -30 °C is compared with the maximum noise that would correspond to an acceptable signal-to-noise ratio after the expected radiation damage from operation at HL-LHC. The measured charge distributions at +30 °C and -30 °C are compared with GEANT4 simulations. The detection efficiency and noise-occupancy were measured as a function of threshold at both +30 °C and -30 °C. The average cluster width was measured as a function of threshold. Scans of detection efficiency versus threshold at different delay settings were used to reconstruct the pulse shape in time. The resulting pulse shape was compared with a circuit model calculation.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.248
Teacher spread0.228 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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