Study of W Boson Production in Association with Two Jets using Boosted Decision Trees with Data Collected by the ATLAS Detector.
Bibliographic record
Abstract
This thesis presents two fiducial detector-level cross section measurements of the production of a W boson in association with two jets (W jj) in proton-proton collisions within the ATLAS detector.To heighten the precision and accuracy of the measurements, boosted decision trees are employed.Firstly, the joint electroweak and strong production rate of W jj events is evaluated, resulting in a detector-level cross section of 4629 ± 6 (stat.)± 137 (syst.)± 80 (lumi.)fb.Secondly, a measure of the production rate of only the electroweak W jj events is found, for which a cross section of 315 ± 3 (stat.)± 31 (syst.)± 5 (lumi.)fb is observed.As a fundamental interaction of the Standard Model, electroweak W jj is of particular interest due to its sensitivity to vector boson fusion and ultimately the triple gauge coupling factors, whose study is at the foremost frontier of particle physics research.i Feynman Diagram Graphical illustration depicting the inner mechanism of particle interactions.Training in machine learning Iteratively updating the parameters of a machine learning algorithm so that it may better perform the desired task.Learning in machine learning A term used to describe the process of training.Classifier An algorithm used for identifying and separating two classes of data.Weak learner A classifier that performs slightly better than random guessing. BoostingThe sequential training and addition of a weak learner to the overall classifier, such that each subsequent learner is trained on the residuals of the previous learner.Hyperparameter Parameters of a machine learning algorithm that have an effect on training when changed.Decision tree A series of nodes connected by binary pass or fail splitting operations, resulting in an inverted tree structure when illustrated graphically.xv
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".