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Tracking within Hadronic Showers in the CALICE SDHCAL prototype using a Hough Transform Technique

2017· article· en· W2613510030 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Instrumentation · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsMcGill University
FundersSecretaría de Estado de Investigación, Desarrollo e InnovaciónCentre National de la Recherche ScientifiqueFonds Wetenschappelijk OnderzoekFonds De La Recherche Scientifique - FNRSBundesministerium für Bildung und ForschungAgence Nationale de la RechercheDeutsche ForschungsgemeinschaftNational Research FoundationCERNAlexander von Humboldt-Stiftung
KeywordsGranularityHadronTracking (education)Hough transformPhysicsCalorimeter (particle physics)Track (disk drive)Energy (signal processing)Computer scienceNuclear physicsOpticsArtificial intelligenceImage (mathematics)Detector

Abstract

fetched live from OpenAlex

The high granularity of the CALICE Semi-Digital Hadronic CALorimeter (SDHCAL) provides the capability to reveal the track segments present in hadronic showers. These segments are then used as a tool to probe the behaviour of the active layers in situ, to better reconstruct the energy of these hadronic showers and also to distinguish them from electromagnetic ones. In addition, the comparison of these track segments in data and the simulation helps to discriminate among the different shower models used in the simulation. To extract the track segments in the showers recorded in the SDHCAL, a Hough Transform is used after being adapted to the presence of the dense core of the hadronic showers and the SDHCAL active medium structure.

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.

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 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 score0.686
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.323
Teacher spread0.284 · 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