Modelling $Z\\to\\tau\\tau$ processes in ATLAS with $\\tau$-embedded $Z\\to\\mu\\mu$ data
Bibliographic record
Abstract
This paper describes the concept, technical realisation and validation of a largely data-driven method to model events with $Z\\to\\tau\\tau$ decays. In $Z\\to\\mu\\mu$ events selected from proton-proton collision data recorded at $\\sqrt{s}=8$ TeV with the ATLAS experiment at the LHC in 2012, the $Z$ decay muons are replaced by $\\tau$ leptons from simulated $Z\\to\\tau\\tau$ decays at the level of reconstructed tracks and calorimeter cells. The $\\tau$ lepton kinematics are derived from the kinematics of the original muons. Thus, only the well-understood decays of the $Z$ boson and $\\tau$ leptons as well as the detector response to the $\\tau$ decay products are obtained from simulation. All other aspects of the event, such as the $Z$ boson and jet kinematics as well as effects from multiple interactions, are given by the actual data. This so-called $\\tau$-embedding method is particularly relevant for Higgs boson searches and analyses in $\\tau\\tau$ final states, where $Z\\to\\tau\\tau$ decays constitute a large irreducible background that cannot be obtained directly from data control samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".