Estimating deceiving signatures and their role in the observation of the VBF production mode in the Higgs-Boson decay into two W bosons
Bibliographic record
Abstract
The Higgs boson is the most recently discovered fundamental particle and its precise characterization is an important test of the Standard Model. For these measurements, the decay into two W bosons is crucial because of the large branching ratio, the sensitivity to vector-boson couplings and the theoretical implications on WW scattering. The proton-proton collisions at the LHC with a centre-of-mass energy of √s = 13 TeV provide an excellent environment to study the Higgs boson. The data recorded by the ATLAS experiment between 2015 and 2018 is analyzed. With an integrated luminosity of 139/fb, it allows to measure precisely the two dominant production modes, gluon fusion (ggF) and vector-boson fusion (VBF). Only collisions with one electron and one muon in the final state are considered and cuts are applied to select signal-like events. For the VBF process, the selection is further refined with the help of a deep neural network. Then, the purified dataset is analyzed with a statistical model constructed from MC simulation and a data-driven background estimate of misidentified leptons. The cross sections times branching ratio are measured to be 12.4 ± 1.5 pb for the ggF and 0.79+0.19−0.16 pb for the VBF processes. For the first time, the VBF process is observed in this decay channel with a significance of 6.6 standard deviations. Also measurements of eleven Simplified Template Cross Sections are performed and found to be consistent with the Standard Model. This precise measurement could only be achieved because the treatment of events with misidentified leptons was improved significantly. These deceiving signatures are estimated with the data-driven Fake Factor Method. An auxiliary measurement is performed to extract fake factors in a phase space with a Z boson recoiling against a jet that is misidentified as lepton. This measurement is presented together with the relevant experimental improvements. As a result of this work, the uncertainties could be reduced to an extent that they do not significantly affect the measurement of the Higgs-boson cross section. Even though the Fake Factor Method is experimentally well-established, a consistent derivation for a phase space with many leptons does not exist. In this thesis, it is shown for the first time that the Fake Factor Method can be derived exactly from the Matrix Method for an arbitrary number of misidentified leptons. In addition, the uncertainty estimate is considered and cases are derived in which the fake estimate does not exhibit Gaussian uncertainties.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".