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Record W2788067574 · doi:10.1139/cjp-2017-0766

Energy cutoff effect in CORSIKA on the detected particles of ππ<sup>0</sup> decay channel

2018· article· en· W2788067574 on OpenAlexfundvenueno aff
Şeyma Atik Yılmaz, A. Yılmaz, H. Denizli, K. Y. Oyulmaz

Bibliographic record

VenueCanadian Journal of Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma KurumuMount Royal University
KeywordsPhysicsMuonHadronNuclear physicsElectronRange (aeronautics)CutoffPhotonCosmic rayParticle (ecology)Charged particleKinetic energyEnergy (signal processing)Atomic physicsIonOptics

Abstract

fetched live from OpenAlex

CORSIKA (COsmic Ray SImulations for Kascade) has various features in the creation of the extensive air showers with several characteristics where the users can configure the input card to achieve the desired result. ECUT is a parameter defined as the low energy cutoff of the particle kinetic energy for hadrons, muons, electrons, and photons. We investigate the effect of the different ECUT values on the detected particles in 107 – 5 × 108 GeV primary particle energy range where π–π0 decay channel chosen with 5 km shower development length. The selected ECUT values in this study are 0.05 and 0.1 GeV for both hadrons and muons and 0.05 GeV for electrons and photons. The effect of the reducing by half on the energy cutoff value is studied for the detector array located on an inclined plane, which is planned for upward τ detection.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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 designSimulation or modeling
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 routes2
Has abstractyes

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