Measurement of inclusive dijet cross-sections in proton-proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector
Bibliographic record
Abstract
Inclusive dijet cross-sections have been measured in proton-proton collisions at a centre-of-mass energy of 13 TeV using data with an integrated luminosity of 140 fb$^{-1}$, recorded by the ATLAS detector at the Large Hadron Collider during 2015-2018. Jets are identified using the anti-$k_{t}$ algorithm with a radius parameter of $R = 0.4$. The inclusive dijet double-differential cross-sections are measured first as a function of the invariant dijet mass and the half absolute rapidity separation between the two leading jets, $(m_{\mathrm{jj}}$, $y^{\ast})$, and second as a function of the invariant dijet mass and the total longitudinal boost of the dijet system, $(m_{\mathrm{jj}}$, $y_{\mathrm{boost}})$. The measured dijet system covers the invariant mass range from 240 GeV to almost 10 TeV, with dijet separation $y^{\ast} < 3.0$ and dijet boost $y_{\mathrm{boost}} < 3.0$. The results are unfolded to the particle level and compared with state-of-the-art next-to-next-to-leading-order full colour perturbative QCD calculations, corrected for non-perturbative and electroweak effects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".