Study of hard double-parton scattering in four-jet events in $pp$ collisions at $\sqrt{s} = 7$ TeV with the ATLAS experiment
Bibliographic record
Abstract
Inclusive four-jet events produced in proton--proton collisions at a centre-of-mass energy of sqrt{s} = 7 TeV are analysed for the presence of hard double-parton scattering using data corresponding to an integrated luminosity of 37.3 pb^-1, collected with the ATLAS detector at the LHC. The contribution of hard double-parton scattering to the production of four-jet events is extracted using an artificial neural network, assuming that hard double-parton scattering can be approximated by an uncorrelated overlaying of dijet events. For events containing at least four jets with transverse momentum p_T > 20 GeV and pseudorapidity eta < 4.4, and at least one having p_T > 42.5 GeV, the contribution of hard double-parton scattering is estimated to be f_{DPS} = 0.092 ^{+0.005}_{-0.011} (stat.) ^{+0.033}_{-0.037} (syst.). After combining this measurement with those of the inclusive dijet and four-jet cross-sections in the appropriate phase space regions, the effective cross-section, sigma_{eff}, was determined to be sigma_{eff} = 14.9 ^{+1.2}_{-1.0} (stat.) ^{+5.1}_{-3.8} (syst.) mb. This result is consistent within the quoted uncertainties with previous measurements of sigma_{eff}, performed at centre-of-mass energies between 63 GeV and 8 TeV using various final states, and it corresponds to 21^{+7}_{-6}% of the total inelastic cross-section measured at sqrt{s} = 7 TeV. The distributions of the observables sensitive to the contribution of hard double-parton scattering, corrected for detector effects, are also provided UPDATE 29/09/2016: The normalisation of all plots was changed to take into account the bin width correctly. As all distributions have a constant bin width, the change is a trivial overall scaling factor for all distributions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".