Record W2914029004 · doi:10.1016/j.nima.2019.05.013

Analysis of testbeam data of the highly granular RPC-steel CALICE digital hadron calorimeter and validation of Geant4 Monte Carlo models

2019· article· en· W2914029004 on OpenAlexfundno aff

M. Chefdeville, J. Repond, J. Schlereth, J.R. Smith, D. Trojand, Q. Zhang, J. Apostolakis, C. Grefe, V. Ivantchenko, G. Folger, A. Ribon, V. Uzhinskiy, G. Blazey, A. Dyshkant, K. Francis, V. Zutshi, O. Bach, Vladimir Bocharnikov, E. Brianne, K. Gadow, P. Göttlicher, Ioannis D. Gialamas, Daniel Heuchel, F. Kriváň, K. Krüger, J. Kvasnicka, S. L. Lu, C. Neubüser, O. Pinto, Ambra Provenza, M. Reinecke, S. Schuwalow, Y. Sudo, H.L. Tran, P. Buhmann, E. Garutti, D. Lomidze, S. Martens, M. Matysek, B. Bilki, D. Northacker, Y. Onel, B. van Doren, G. Wilson, K. Kawagoe, Yoshio Miura, R. Mori, I. Sekiya, Taikan Suehara, T. Yoshioka, D. Belver, E. Calvo, M. C. Fouz, Héctor García Cabrera, J. Marín, J. Navarrete, J. Puerta Pelayo, A. Verdugo, F. Corriveau, B. Freund, M. Chadeeva, M. Danilov, M. Gabriel, L. Emberger, C. Graf, Y. Israeli, F. Simon, M. Szalay, H. Windel, S. Bilokin, J. Bonis, A. Irles Quiles, R. Pöschl, A. Thiebault, F. Richard, D. Zerwas, J. Cvach, M. Janata, M. Kovalcuk, I. Polák, J. Smolik, V. Vrba, J. Zálešâk, J. Zuklin, T. Takeshita, Amine Elkhalii, M. Götze, C. Zeitnitz, S. Chang, Arshad Khan, D.H. Kim, D. J. Kong, Y. D. Oh

Bibliographic record

VenueNuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · 2019

Typearticle

Languageen

FieldPhysics and Astronomy

TopicParticle Detector Development and Performance

Canadian institutionsnot available

FundersPacific Northwest National LaboratoryFermilabDeutsche ForschungsgemeinschaftHigh Energy PhysicsScience and Technology Facilities CouncilInstitut National de Physique Nucléaire et de Physique des ParticulesNuclear PhysicsNatural Sciences and Engineering Research Council of CanadaHelmholtz-GemeinschaftDeutsches Elektronen-SynchrotronNational Research Foundation of KoreaCenter for Cosmology and Astroparticle Physics, Ohio State UniversityOffice of ScienceIsrael Science FoundationNella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of ScienceFonds Wetenschappelijk OnderzoekPlanning and Budgeting Committee of the Council for Higher Education of IsraelMinisterstvo Školství, Mládeže a TělovýchovyBundesministerium für Bildung und ForschungCentre National de la Recherche ScientifiqueIsraeli Centers for Research ExcellenceU.S. Department of EnergyCERNNational Research FoundationAlexander von Humboldt-Stiftung

KeywordsPhysicsCalorimeter (particle physics)Nuclear physicsFermilabMonte Carlo methodMuonHadronContext (archaeology)DetectorParticle physicsOptics

DOI10.1016/j.nima.2019.05.013

Abstract

fetched live from OpenAlex

No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002

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: Empirical

Teacher disagreement score0.006

Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

Category	Codex	Gemma
Metaresearch	0.001	0.002
Meta-epidemiology (narrow)	0.001	0.000
Meta-epidemiology (broad)	0.001	0.001
Bibliometrics	0.002	0.003
Science and technology studies	0.001	0.000
Scholarly communication	0.001	0.000
Open science	0.001	0.001
Research integrity	0.001	0.000
Insufficient payload (model declined to judge)	0.006	0.001

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 head0.065

GPT teacher head0.355

Teacher spread0.290 · 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

Citations17

Published2019

Admission routes1

Has abstractno

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