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Record W2553662149

A silicon sensor study for the ILD ECAL

2014· article· en· W2553662149 on OpenAlexaff
Tatsuhiko Tomita, H. Hirai, K. Kawagoe, Yohei Miyazaki, Yuji Sudo, Taikan Suehara, Hiroki Sumida, Shusuke Takada, Hiraku Ueno, T. Yoshioka

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsCalorimeter (particle physics)DetectorGranularityCapacitanceSiliconOpticsPhysicsTungstenMaterials scienceOptoelectronicsElectrical engineeringElectronic engineeringEngineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

The International Large Detector (ILD) is a proposed detector for the International Linear Collider (ILC). It has been designed to achieve an excellent jet energy resolution by using Particle Flow Algorithm (PFA), which relies on the ability to separate nearby particles within a jet. PFA requires the calorimeters with high granularity. A sampling calorimeter with tungsten plates and silicon sensors is proposed for the Electromagnetic Calorimeter (ECAL). Thirty layers of tungsten plates, with total thickness of about 24 X0, are chosen for small Moliere radius to minimize overlap of electromagnetic showers. Fine granularity is achieved by using silicon sensors having 256 pixels of 5.5×5.5 mm 2 in an area of 9×9 cm 2 . The total number of readout channels amounts to the order of 10 8 . We have measured various properties of these prototype sensors: the leakage current, capacitance, and full depletion voltage. To optimize the sensor design, we have also examined relation of the guard ring structure and the cross talk between pixels using an infrared laser system.

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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.018
GPT teacher head0.260
Teacher spread0.242 · 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
Published2014
Admission routes1
Has abstractyes

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