Search for the associated Higgs boson production with top quarks in CMS
Bibliographic record
Abstract
A precise determination of the Yukawa coupling to top quarks is crucial for the characterization of the recently discovered Higgs boson. A direct measurement of this coupling can be provided only by those processes where the Higgs boson is produced in association with top quarks. In the Standard Model (SM), the production of the Higgs boson together with a top-antitop pair (tt‾H) has the largest cross section, followed by the production with a single top quark (tH), with roughly one order of magnitude smaller rate. As of summer 2014, the CMS experiment at LHC has performed searches for either processes using the full Run-I data set in a variety of decay channels of the Higgs boson, namely H→WW⁎,ZZ⁎,τ+τ−,bb‾,γγ for the tt‾H production, and H→γγ for tH. Due to the small cross section, the sensitivity to the SM tH production is still far from being achieved, yet stringent constraints can be already set for non-SM couplings. Conversely, the combination of the various tt‾H searches has already attained sensitivity to measure a SM-like signal, providing a best-fit value of the signal strength modifier μˆ=2.76−0.92+1.05, compatible with the SM expectation at the 2σ level. An improved tt‾H search in the H→γγ channel using the full 7 and 8 TeV data set, as well as a new tt‾H, H→bb‾ analysis using a matrix element technique, are here presented for the first time.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".