Underlying event characteristics and their dependence on jet size of charged-particle jet events inppcollisions at(s)=<mml:…
Bibliographic record
Abstract
Distributions sensitive to the underlying event are studied in events containing one or more charged-particle jets produced in $pp$ collisions at $\sqrt{s}=7\text{ }\text{ }\mathrm{TeV}$ with the ATLAS detector at the Large Hadron Collider (LHC). These measurements reflect $800\text{ }\text{ }\ensuremath{\mu}{\mathrm{b}}^{\ensuremath{-}1}$ of data taken during 2010. Jets are reconstructed using the anti-${k}_{t}$ algorithm with radius parameter $R$ varying between 0.2 and 1.0. Distributions of the charged-particle multiplicity, the scalar sum of the transverse momentum of charged particles, and the average charged-particle ${p}_{\mathrm{T}}$ are measured as functions of ${p}_{\mathrm{T}}^{\mathrm{jet}}$ in regions transverse to and opposite the leading jet for $4\text{ }\text{ }\mathrm{GeV}<{p}_{\mathrm{T}}^{\mathrm{jet}}<100\text{ }\text{ }\mathrm{GeV}$. In addition, the $R$ dependence of the mean values of these observables is studied. In the transverse region, both the multiplicity and the scalar sum of the transverse momentum at fixed ${p}_{\mathrm{T}}^{\mathrm{jet}}$ vary significantly with $R$, while the average charged-particle transverse momentum has a minimal dependence on $R$. Predictions from several Monte Carlo tunes have been compared to the data; the predictions from Pythia 6, based on tunes that have been determined using LHC data, show reasonable agreement with the data, including the dependence on $R$. Comparisons with other generators indicate that additional tuning of soft-QCD parameters is necessary for these generators. The measurements presented here provide a testing ground for further development of the Monte Carlo models.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".