Dark matter signals in dilepton production at hadron colliders
Bibliographic record
Abstract
We show that new physics can show up in dileptonic events through its radiative contributions to the dilepton invariant mass, leading to unique ``monocline'' features in ${m}_{\ensuremath{\ell}\ensuremath{\ell}}$, as well as the angular distribution of the leptons. We focus in particular on the case of dark matter with scalar messengers coupling it to the quarks and leptons. Consistent thermal models require the dark matter to have masses of 100s of GeV and have $\ensuremath{\gtrsim}1$ couplings to the Standard Model (SM), implying that radiative corrections to the SM Drell-Yan rate can be sizable. We consider the case of Majorana, Dirac, and pseudo-Dirac dark matter and show that there are regions of parameter space where the nonexistence of a monocline, which starts at roughly twice the dark matter mass, ${m}_{\ensuremath{\ell}\ensuremath{\ell}}\ensuremath{\sim}2{m}_{\ensuremath{\chi}}$, places the strongest constraint on the model. We make predictions for the sensitivities at the high luminosity 14 TeV LHC as well as a future 100 TeV proton-proton collider. We find that our dilepton signal is most sensitive when the mediator and the dark matter are nearly degenerate and conventional missing-transverse-momentum-based searches are least sensitive.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".