Improvement to the statistical sensitivity of top quark pair production in conjunction with additional heavy flavour jets through multivariate analysis
Bibliographic record
Abstract
With the mass of the discovered Higgs-like boson being 125 GeV, this leads to a primary Higgs decay mode to two bottom (b) jets. A precise measurement of top-pair (tt̄) production in conjunction with two additional b-jets is essential to reduce the background uncertainty on the tt̄ + Higgs production cross-section, a direct probe of the Higgs to Yukawa coupling. This thesis attempts to improve on the statistical sensitivity of tt̄ production in conjunction with two additional heavy-flavour jets, using expected sensitivities from 20.3 fb-¹ of pp collision data at √s = 8TeV, collected by the ATLAS detector at the Large Hadron Collider in 2012. This thesis compares multiple multivariate analysis techniques, boosted decision trees and artificial neural networks, in both binary and multi-class classification cases. An overall improvement in precision was seen, from 19.7% uncertainty on the baseline tt̄ + bb̄ measurement based on a fit to the best single variable, to 16.1% uncertainty with the very best multi-class neural network algorithm. This represents a relative improvement of nearly 20% and could thus reduce luminosity needed for a precision measurement of this process.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".