Performance of the jet energy calibration at ATLAS using pt balance in Z plus jet events
Bibliographic record
Abstract
A precision measurement of the jet energy scale is essential for the success of the ATLAS experiment.This thesis investigates the suitability of p T balancing in Z + Jet events as an in situ technique for assessing the performance of the jet energy calibration.While the technique is shown to have a kinematic bias in the region p Z T < 60 GeV/c, it is useful for studying jet performance at higher p Z T .The effects of background processes and signal selection criteria on the p T balance are studied.This study also investigates the performance of jet reconstruction with various jet input constituents, jet algorithms and sizes, and jet calibration schemes.iv ABR ÉG É Une mesure précise de l'échelle d'énergie des jets est indispensable pour la réussite de l'expérience ATLAS.La présente thèse examine la viabilité de l'équilibrage en p T dans les événements Z + Jet en tant que technique in situ pour l'estimation de la performance de la calibration des jets.Bien que cette technique s'avère biaisée dans la région cinématique p Z T < 60 GeV/c, il est démontré qu'elle est utile dans un régime à haut p T .Les effets des différentes contributions au bruit de fond ainsi que les critères de sélection du signal sur l'équilibrage en p T sont étudiés.Cette étude examine aussi la performance de la reconstruction des jets avec différents constituants de jets, algorithmes et méthodes de calibration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".