Search for chargino-neutralino production using recursive jigsaw reconstruction in final states with two orpthree charged leptons in proton-proton collisions at √s = 13 TeVwith the ATLAS detector
Bibliographic record
Abstract
A search for electroweak production of supersymmetric particles is performed in two-lepton and three- lepton final states using recursive jigsaw reconstruction, a technique that assigns reconstructed objects to the most probable hemispheres of the decay trees, allowing one to construct tailored kinematic variables to separate the signal and background. The search uses data collected in 2015 and 2016 by the ATLAS experiment in √s = 13 TeV proton-proton collisions at the CERN Large Hadron Collider corresponding to an integrated luminosity of 36.1 fb⁻¹. Chargino-neutralino pair production, with decays via W=Z bosons, is studied in final states involving leptons and jets and missing transverse momentum for scenarios with large and intermediate mass splittings between the parent particle and lightest supersymmetric particle, as well as for the scenario where this mass splitting is close to the mass of the Z boson. The latter case is challenging since the vector bosons are produced with kinematic properties that are similar to those in Standard Model processes. Results are found to be compatible with the Standard Model expectations in the signal regions targeting large and intermediate mass splittings, and chargino-neutralino masses up to 600 GeV are excluded at 95% confidence level for a massless lightest supersymmetric particle. Excesses of data above the expected background are found in the signal regions targeting low mass splittings, and the largest local excess amounts to 3.0 standard deviations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".