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Record W2077725864 · doi:10.1139/p08-049

Power laws and production of direct photons in high-energy physics

2008· article· en· W2077725864 on OpenAlexvenueno aff
Saikat Biswas, Goutam Sau, P. Guptaroy, Bhaskar De, A. Bhattacharya, S. Bhattacharyya

Bibliographic record

VenueCanadian Journal of Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsPhotonHadronSpectral lineNuclear physicsRange (aeronautics)Power lawElementary particleWork (physics)Momentum (technical analysis)Particle physicsComputational physicsAtomic physicsQuantum electrodynamicsQuantum mechanicsStatistics

Abstract

fetched live from OpenAlex

The measured data on transverse momentum spectra for production of direct photons in hadron–hadron, hadron–nucleus, and nucleus–nucleus interactions at high energies are compiled. Besides, data on some other aspects related to these p T -spectra are also assorted in the present work. The p T -spectra for production of direct photons at p T > 1 GeV/c are, then, fitted here by using two parameterizations, which essentially approach the power-law dependence at high transverse momenta. The power indices range between 16 to 7 depending on the interacting particles and the centre-of-mass energy of the system. The power index decreases gradually with increasing energy, with a variation of the rate of fall for various energy ranges. The formulae applied here phenomenologically bring out the main features of the data characteristics in a modestly satisfactory manner. This provokes us to raise some serious questions about the existing “theoretical” approaches, all, or most of which, lack in the first-principle derivations of the actual working expressions used to finally describe the nature of the measured data on direct photons, or for that matter, on any other secondary particle.PACS Nos.: 25.75.–q, 12.38.Mh, 13.85.Ni

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.995

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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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 designTheoretical or conceptual
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
Published2008
Admission routes1
Has abstractyes

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