Combination of searches for heavy resonances decaying into bosonic and leptonic final states using 36??fb?1 of proton-proton collision data at √s=13??TeV with the ATLAS detector
Bibliographic record
Abstract
Searches for new heavy resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons, are presented using a data sample corresponding to 36.1 fb -1 of pp collisions at ffiffi ffi s p ¼ 13 TeV collected during 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider.Analyses selecting bosonic decay modes in the qqqq, ννqq, lνqq, llqq, lνlν, llνν, lνll, llll, qqbb, ννbb, lνbb, and llbb final states are combined, searching for a narrow-width resonance.Likewise, analyses selecting the leptonic lν and ll final states are also combined.These two sets of analyses are then further combined.No significant deviation from the Standard Model predictions is observed.Three benchmark models are tested: a model predicting the existence of a new heavy scalar singlet, a simplified model predicting a heavy vector-boson triplet, and a bulk Randall-Sundrum model with a heavy spin-2 Kaluza-Klein excitation of the graviton.Cross section limits are set at the 95% confidence level using an asymptotic approximation and are compared with predictions for the benchmark models.These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson.The data exclude a heavy vector-boson triplet with mass below 5.5 TeV in a weakly coupled scenario and 4.5 TeV in a strongly coupled scenario, as well as a Kaluza-Klein graviton with mass below 2.3 TeV.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".