Observation of tt¯ production in the lepton+jets and dilepton channels in p+Pb collisions at sNN = 8.16 TeV with the ATLAS detector
Bibliographic record
Abstract
Abstract This paper reports the observation of top-quark pair production in proton-lead collisions in the ATLAS experiment at the Large Hadron Collider. The measurement is performed using 165 nb −1 of p+Pb data collected at $$ \sqrt{s_{\textrm{NN}}} $$ s NN = 8.16 TeV in 2016. Events are categorised in two analysis channels, consisting of either events with exactly one lepton (electron or muon) and at least four jets, or events with two opposite-charge leptons and at least two jets. In both channels at least one b-tagged jet is also required. Top-quark pair production is observed with a significance over five standard deviations in each channel. The top-quark pair production cross-section is measured to be $$ {\sigma}_{t\overline{t}}=58.1\pm 2.0{\left(\textrm{stat}.\right)}_{-4.4}^{+4.8}\left(\textrm{syst}.\right) $$ σ t t ¯ = 58.1 ± 2.0 stat . − 4.4 + 4.8 syst . nb, with a total uncertainty of 9%. In addition, the nuclear modification factor is measured to be $$ {R}_{p\textrm{A}}=1.090\pm 0.039{\left(\textrm{stat}.\right)}_{-0.087}^{+0.094}\left(\textrm{syst}.\right) $$ R p A = 1.090 ± 0.039 stat . − 0.087 + 0.094 syst . . The measurements are found to be in good agreement with theory predictions involving nuclear parton distribution functions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".