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Record W2606578885 · doi:10.1139/cjp-2016-0911

An investigation of Renyi entropy in high-energy nucleus–nucleus collisions

2017· article· en· W2606578885 on OpenAlexvenueno aff
Swarnapratim Bhattacharyya, Maria Haiduc, Alina Tania Neagu, Elena Firu

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsnot available
FundersUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiAutoritatea Natională pentru Cercetare StiintificăJoint Institute for Nuclear ResearchJadavpur University
KeywordsPhysicsNuclear emulsionLogarithmMultiplicity (mathematics)Rényi entropyProjectileNucleusNuclear physicsEntropy (arrow of time)Atomic physicsThermodynamicsPrinciple of maximum entropyStatisticsQuantum mechanics

Abstract

fetched live from OpenAlex

A study of Renyi entropy of shower particles has been carried out using 16 O, 28 Si, and 32 S projectiles in interaction with AgBr and CNO target present in nuclear emulsion at an incident momentum of 4.5 AGeV/c. The analysis reveals interesting experimental observations for both groups of targets. A comparison of the experimental results with the results obtained from analyzing the event sample generated by the UrQMD code is also presented. Renyi entropy values increase with the increase of projectile mass. This increase is weaker in case of CNO target. The calculated values of second-order Renyi entropy H 2 are shown to vary linearly with the logarithm of average particle multiplicity ln⟨n⟩ for both targets. The dependence of Renyi entropy on the logarithm of average particle multiplicity ln⟨n⟩ indicates the presence of long-range correlation in high-energy nucleus–nucleus interactions. The variation is found to be stronger in the case of experimental events in comparison to the UrQMD simulated events indicating stronger long-range correlation for the experimental data.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.021
GPT teacher head0.272
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2017
Admission routes1
Has abstractyes

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