Identification and energy calibration of hadronically decaying tau leptons with the ATLAS experiment in pp collisions at $$\sqrt{s}=8$$ s = 8 $$\,\hbox {TeV}$$ TeV
Bibliographic record
Abstract
This paper describes the trigger and offline reconstruction, identification and energy calibration algorithms for hadronic decays of tau leptons employed for the data collected from pp collisions in 2012 with the ATLAS detector at the LHC center-of-mass energy $$\sqrt{\mathrm {s}} = 8$$ $$\,\hbox {TeV}$$ . The performance of these algorithms is measured in most cases with $$Z$$ decays to tau leptons using the full 2012 dataset, corresponding to an integrated luminosity of 20.3 fb $$^{-1}$$ . An uncertainty on the offline reconstructed tau energy scale of 2–4 %, depending on transverse energy and pseudorapidity, is achieved using two independent methods. The offline tau identification efficiency is measured with a precision of 2.5 % for hadronically decaying tau leptons with one associated track, and of 4 % for the case of three associated tracks, inclusive in pseudorapidity and for a visible transverse energy greater than 20 $$\,\hbox {GeV}$$ . For hadronic tau lepton decays selected by offline algorithms, the tau trigger identification efficiency is measured with a precision of 2–8 %, depending on the transverse energy. The performance of the tau algorithms, both offline and at the trigger level, is found to be stable with respect to the number of concurrent proton–proton interactions and has supported a variety of physics results using hadronically decaying tau leptons at ATLAS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".