Probing electroweak pair production of heavy neutral leptons with displaced vertices at the LHC
Bibliographic record
Abstract
We study the sensitivity of displaced vertex searches at the LHC to heavy neutral leptons (also known as sterile neutrinos) that are produced in pairs with an electroweak-size cross section. We work within the context of a supersymmetric model in which the sterile neutrino is produced along with Standard Model particles in higgsino decays. By making use of model-independent reconstruction efficiencies provided by the ATLAS collaboration in their search for displaced vertices with multiple jets, we obtain constraints on this model from $139$ fb$^{-1}$ of data collected by ATLAS during the LHC Run~2, and assess the discovery reach of Run~3 and of the high-luminosity LHC (HL-LHC). Depending on the higgsino mass parameter, sterile neutrino masses between $20~\mathrm{GeV}$ and $230~\mathrm{GeV}$ and active-sterile neutrino mixings in the range $4 \times 10^{-14} \lesssim V^2_N \lesssim 3 \times 10^{-10}$ can be excluded. At the HL-LHC, discovery-level significances could be reached for sterile neutrinos masses up to $295~\mathrm{GeV}$ and values of $V^2_N$ down to $3 \times 10^{-14}$. Finally, moving away from the supersymmetric scenario, we study to which extent these results can be generalized to a broader class of models in which the sterile neutrinos are produced in the decays of heavier particles that are themselves pair-produced with an electroweak-size cross section.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".