Search for Gluinos and Squarks Using Like-Sign Dileptons in pp¯ Collisions at s=1.8 TeV
Bibliographic record
Abstract
We present results of the first search for like-sign dilepton ( ${e}^{\ifmmode\pm\else\textpm\fi{}}{e}^{\ifmmode\pm\else\textpm\fi{}}$, ${e}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}}$, ${\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}}$) events associated with multijets and large missing energy using $106{\mathrm{pb}}^{\ensuremath{-}1}$ of data in $p\overline{p}$ collisions at $\sqrt{s}\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}1.8\mathrm{TeV}$ collected during 1992--1995 by the CDF experiment. Finding no events that pass our selection, we examine pair production of gluinos ( $\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{g}$) and squarks ( $\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{q}$) in a constrained framework of the minimal supersymmetric standard model. At $\mathrm{tan}\ensuremath{\beta}\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}2$ and $\ensuremath{\mu}\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}\ensuremath{-}800\mathrm{GeV}{/c}^{2}$, we set $95%$ confidence level limits of ${M}_{\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{g}}>221\mathrm{GeV}{/c}^{2}$ for ${M}_{\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{g}}\ensuremath{\simeq}{M}_{\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{q}}$, and ${M}_{\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{g}}>168\mathrm{GeV}{/c}^{2}$ for ${M}_{\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{q}}\ensuremath{\gg}{M}_{\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{g}}$, both with small variation as a function of $\ensuremath{\mu}$.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".