Bibliographic record
Abstract
Several outstanding problems currently not addressed by the Standard Model (SM) of particle physics including neutrino masses, matter-antimatter asymmetry and dark matter could be successfully addressed by the addition of right-handed neutrinos with Majorana masses below the electroweak scale. In this thesis leptonic decays of the W boson produced in $36.1~\mathrm{fb}^{-1}$ of $\sqrt{s} = 13~\mathrm{TeV}$ proton-proton collisions at the Large Hadron Collider (LHC) are searched for Heavy Neutral Leptons (HNLs). The HNLs are produced through mixing with muon or electron neutrinos, and detected in the ATLAS detector through their leptonic decays. The HNL Majorana nature is used to find the smoking-gun signature of three leptons $\ell^\pm \ell\pm \ell'^\mp$, $\ell, \ell' = e, \mu$ otherwise not observed in decays of SM particles. This signature allows a search for HNLs in mass ranges of $5$ to $50~\mathrm{GeV}$ that decay close to the beamline. This search sets best constraints on the mixing angle in the HNL mass range $15$ to $42~\mathrm{GeV}$ for the mixing of HNLs with muon neutrinos and for $25$ to $50~\mathrm{GeV}$ for the mixing with electron neutrinos and is the first result in the mass range $5$ to $50~\mathrm{GeV}$ using data measured by ATLAS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".