Search for new phenomena using the invariant mass distribution of same-flavour opposite-sign dilepton pairs in events with missing transverse momentum in $\sqrt{s}=13$ TeV $pp$ collisions with the ATLAS detector
Bibliographic record
Abstract
A search for new phenomena in final states containing an $e^+e^-$ or $\mu^+\mu^-$ pair, jets, and large missing transverse momentum is presented. Thisadditional_resources: analysis makes use of proton-proton collision data, collected during 2015 and 2016 at a centre-of-mass energy $\sqrt{s}=13$ TeV with the ATLAS detector at the Large Hadron Collider, which corresponds to an integrated luminosity of 36.1 fb$^{-1}$. The search targets the pair production of supersymmetric coloured particles, squarks or gluinos, and their decays to final states containing an $e^+e^-$ or $\mu^+\mu^-$ pair and the lightest neutralino ($\tilde{\chi}_1^0$) via one of two next-to-lightest neutralino ($\tilde{\chi}_2^0$) decay mechanisms: $\tilde{\chi}_2^0 \rightarrow Z \tilde{\chi}_1^0$, where the $Z$ boson decays leptonically leading to a peak in the dilepton invariant mass distribution around the $Z$ boson mass; and $\tilde{\chi}_2^0 \rightarrow \ell^+\ell^- \tilde{\chi}_1^0$ with no intermediate $\ell^+\ell^-$ resonance, yielding a kinematic endpoint in the dilepton invariant-mass spectrum. The data are found to be consistent with the Standard Model expectation. Results are interpreted using simplified models, and exclude gluinos and squarks with masses as large as 1.85 TeV and 1.3 TeV at 95% confidence level, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".