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Record W6965247272 · doi:10.3204/pubdb-2018-05851

Construction and response of a highly granular scintillator-based electromagnetic calorimeter

2018· article· en· W6965247272 on OpenAlexfundno aff

Bibliographic record

VenueDESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron) · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsnot available
FundersFermilabHigh Energy PhysicsJapan Society for the Promotion of ScienceNuclear PhysicsNatural Sciences and Engineering Research Council of CanadaFonds Wetenschappelijk OnderzoekPlanning and Budgeting Committee of the Council for Higher Education of IsraelBundesministerium für Bildung und ForschungMinisterstvo Školství, Mládeže a TělovýchovyDeutsche ForschungsgemeinschaftScience and Technology Facilities CouncilNational Research FoundationU.S. Department of EnergyEuropean CommissionAlexander von Humboldt-StiftungIsraeli Centers for Research ExcellenceNational Research Foundation of KoreaMinisterio de Economía y CompetitividadNational Science Foundation
KeywordsFermilabScintillatorCalorimeter (particle physics)Energy (signal processing)ColliderLinear particle acceleratorBeam (structure)Superconducting Super ColliderDetector

Abstract

fetched live from OpenAlex

A highly granular electromagnetic calorimeter with scintillator strip readout is being developed for future linear collider experiments. A prototype of 21.5 $X_0$depth and $180 \times 180$ $mm^2$ transverse dimensions was constructed, consisting of 2160 individually read out $10\times 45\times 3$ $mm^3$ scintillator strips. This prototype was tested using electrons of 2–32 $Ge V$ at the Fermilab Test Beam Facility in 2009. Deviations from linear energy response were less than 1.1%, and the intrinsic energy resolution was determined to be $(12.5\pm 0.1(stat.)\pm 0.4(syst.))\%$ $/$ $\sqrt{E[Ge V]}\oplus \left( 1.2\pm 0.1(stat.)_{-0.7}^{+0.6}(syst.)\right)\%$, where the uncertainties correspond to statistical and systematic sources, respectively.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.185
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.236
Teacher spread0.226 · 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.

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
Published2018
Admission routes1
Has abstractyes

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