Estimation of the fake background for the doubly charged Higgs boson production in the ATLAS experiment
Bibliographic record
Abstract
The existence of doubly charged Higgs bosons (H±±) is predicted by several theories beyond the Standard Model that aim to explain the origin of neutrino masses. These particles are expected to decay into same-sign lepton pairs H±± → ℓ±ℓ±. At the LHC, they are produced predominantly via the Drell–Yan process, resulting in four-lepton final states. Due to the rarity of events involving doubly charged Higgs bosons, an accurate estimation of background contributions is crucial. This thesis focuses on estimating the fake-lepton background using the fake factor method. The analysis is based on 13 TeV ATLAS Open Data events with exactly one lepton, corresponding to an integrated luminosity of 10.06 ± 0.37 fb−1. Separate sets of fake factors were measured for electrons and muons in bins of transverse momentum and pseudorapidity to account for their kinematic dependence. The method was validated by closure tests performed separately for each lepton flavor in regions orthogonal to those used for measurement, but with the same lepton multiplicity. The predicted fake background shows agreement with the observed data at low pT, while discrepancies appear at high pT, likely due to unquantified systematic 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".