Search for electroweak-scale dijet resonances using trigger-level analysis with the ATLAS detector in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>132</mml:mn> <mml:mtext> </mml:mtext> <mml:mtext> </mml:mtext> <mml:msup> <mml:mi>fb</mml:mi> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math> of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>p</mml:mi> <mml:mi>p</mml:mi> </mml:math> collisions at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>13</mml:mn> <mml:mtext> </mml:mtext> <mml:mtext> </mml:mtext> <mml:mi>TeV</mml:mi> </mml:math>
Bibliographic record
Abstract
This article reports on a search for dijet resonances using 132 fb − 1 of p p collision data recorded at s = 13 TeV by the ATLAS detector at the Large Hadron Collider. The search is performed solely on jets reconstructed within the ATLAS trigger to overcome bandwidth limitations imposed on conventional single-jet triggers, which would otherwise reject data from decays of sub-TeV dijet resonances. Collision events with two jets satisfying transverse momentum thresholds of p T ≥ 85 GeV and jet rapidity separation of | y * | < 0.6 are analysed for dijet resonances with invariant masses from 375 to 1800 GeV . A data-driven background estimate is used to model the dijet mass distribution from multijet processes. No significant excess above the expected background is observed. Upper limits are set at 95% confidence level on coupling values for a benchmark leptophobic axial-vector Z ′ model and on the production cross section for a new resonance contributing a Gaussian-distributed line-shape to the dijet mass distribution.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".