Estimation of the fake/non-prompt lepton background in the search for a fermiophobic low-mass charged Higgs boson through Wy resonances with ATLAS
Bibliographic record
Abstract
Charged Higgs bosons are a proposed theoretical extension to the Standard Model of particle physics.Currently, a search for a fermiophobic charged Higgs boson H ± 5 with a mass between 110 GeV and 200 GeV through the H ± 5 → W ± γ decay is being developed.The analysis is based on data collected in the ATLAS experiment using proton-proton collisions with a center of mass energy of √ s = 13 TeV and an integrated luminosity of 140 fb -1 .The final state of the H ± 5 → W ± γ → ℓ ± ν ℓ γ decay consists of one electron or muon and at least one photon.This analysis is therefore subject to a fake/non-prompt lepton background which includes events where charged leptons produced in semi-leptonic decays of hadrons or through photon conversions pass the lepton selection criteria.Additionally, other objects in the detector can be misreconstructed as electrons or muons.This thesis presents a data-driven estimate of the fake/non-prompt lepton background for the H ± 5 → W ± γ analysis consistent with ATLAS-internal guidelines and practices.The fake factor method relies on defining a control region where the fake efficiency, the probability that a fake/non-prompt lepton passes the selection criteria used in the analysis, is measured.The fake efficiency is then extrapolated into the analysis regions, where the expected background can be calculated.Statistical and systematic uncertainties on the fake/non-prompt lepton background estimates are evaluated.I I'm extremely grateful to my supervisors, Professor Dr. Francois Corriveau and Professor Dr. Andreas Warburton, for allowing me to join the McGill ATLAS group, providing me an interesting research project, and patiently supporting me throughout my Master'
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".