Experimental studies of unbiased gluon jets from<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>−</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math>annihilations using the jet boost algorithm
Bibliographic record
Abstract
We present the first experimental results based on the jet boost algorithm, a technique to select unbiased samples of gluon jets in ${e}^{+}{e}^{\ensuremath{-}}$ annihilations, i.e. gluon jets free of biases introduced by event selection or jet finding criteria. Our results are derived from hadronic ${Z}^{0}$ decays observed with the OPAL detector at the LEP ${e}^{+}{e}^{\ensuremath{-}}$ collider at CERN. First, we test the boost algorithm through studies with HERWIG Monte Carlo events and find that it provides accurate measurements of the charged particle multiplicity distributions of unbiased gluon jets for jet energies larger than about 5 GeV, and of the jet particle energy spectra (fragmentation functions) for jet energies larger than about 14 GeV. Second, we apply the boost algorithm to our data to derive unbiased measurements of the gluon jet multiplicity distribution for energies between about 5 and 18 GeV, and of the gluon jet fragmentation function at 14 and 18 GeV. In conjunction with our earlier results at 40 GeV, we then test QCD calculations for the energy evolution of the distributions, specifically the mean and first two nontrivial normalized factorial moments of the multiplicity distribution, and the fragmentation function. The theoretical results are found to be in global agreement with the data, although the factorial moments are not well described for jet energies below about 14 GeV.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".