Search for long-lived neutral particles in $pp$ collisions at $\sqrt{s}$ = 13 TeV that decay into displaced hadronic jets in the ATLAS calorimeter
Bibliographic record
Abstract
This paper describes a search for pairs of neutral, long-lived particles with masses between $m_S= 5 ~\mathrm{GeV}$ and $40 ~\mathrm{GeV}$ that decay into Standard Model fermions in the ATLAS calorimeter. Such long-lived neutral particles occur in many extensions to the Standard Model. The analysis uses either $10.8 ~\mathrm{fb^{-1}}$ or $33.0 ~\mathrm{fb^{-1}}$ of data (depending on the trigger) recorded in 2016 at the LHC with the ATLAS detector in proton--proton collisions at a centre-of-mass energy of $13 ~\mathrm{TeV}$. No significant excess in data is observed, and limits are reported on the production cross section times branching ratio as a function of the proper decay length of the long-lived particles.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".