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Record W2149075715 · doi:10.1139/p07-195

Pion fluctuation and its multiplicity dependence in ultrarelativistic nuclear collisions

2008· article· en· W2149075715 on OpenAlexvenueno aff
Dipak Ghosh, Argha Deb, Srimonti Dutta

Bibliographic record

VenueCanadian Journal of Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsnot available
FundersUniversity at BuffaloCouncil of Scientific and Industrial Research, India
KeywordsPseudorapidityMultiplicity (mathematics)PhysicsPionAzimuthNuclear physicsParticle physicsCharged particleGeometryOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

This paper studies the multiplicity dependence of the fluctuation pattern of pions produced for 16 O–AgBr interactions at 60 A GeV and 32 S–AgBr interactions at 200 A GeV. We divided the data for the pions produced into four sets depending on the number of shower tracks (n s ). We investigated the fluctuation pattern of emitted pions corresponding to different sets in both η (pseudorapidity) and ϕ (azimuthal angle) space. The multiplicity dependence was studied with respect to parameters α q , D q , multifractal specific heat c, and effective fluctuation strength α eff . The study clearly indicates that the fluctuation pattern depends on multiplicity both in pseudorapidity and azimuthal angle space for 16 O initiated interactions. However, for 32 S initiated interactions, no multiplicity dependence is evidenced by the data in azimuthal angle space. For both, the multiplicity dependence is not very strong and significant only at large average multiplicity. Multiplicity dependence was further studied in a small window around the central pseudorapidity, and we found that multiplicity dependence is more prominent in the central region. The multiplicity dependence of α eff is more pronounced in the case of 32 S–AgBr interactions than in case of 16 O–AgBr interactions. PACS Nos.: 25.75–q, 24.60 Ky

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.429

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 communication0.0000.000
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 head0.027
GPT teacher head0.258
Teacher spread0.231 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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