A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector
Bibliographic record
Abstract
The distribution of particles inside hadronic jets produced in the decay of boosted $W$ and $Z$ bosons can be used to discriminate such jets from the continuum background. Given that a jet has been identified as likely resulting from the hadronic decay of a boosted $W$ or $Z$ boson, this paper presents a technique for further differentiating $Z$ bosons from $W$ bosons. The variables used are jet mass, jet charge, and a b-tagging discriminant. A likelihood tagger is constructed from these variables and tested in the simulation of $W'\rightarrow WZ$ for bosons in the transverse momentum range 200 GeV $<p_{T}<$ 400 GeV in $\sqrt{s}=8$ TeV $pp$ collisions with the ATLAS detector at the LHC. For $Z$-boson tagging efficiencies of $\epsilon_Z=$ 90%, 50%, and 10%, one can achieve $W^+$-boson tagging rejection factors ($1/\epsilon_{W^+}$) of 1.7, 8.3 and 1000, respectively. It is not possible to measure these efficiencies in the data due to the lack of a pure sample of high $p_{T}$, hadronically decaying $Z$ bosons. However, the modelling of the tagger inputs for boosted $W$ bosons is studied in data using a $t\bar{t}$-enriched sample of events in 20.3 fb$^{-1}$ of data at $\sqrt{s}=8$ TeV. The inputs are well modelled within uncertainties, which builds confidence in the expected tagger performance.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".