Bibliographic record
Abstract
Light dark matter with mass smaller than about 10 GeV is difficult to probe from direct detection experiments. In order to have the correct thermal relic abundance, the mediator of the interaction between dark matter and the Standard Model (SM) should also be relatively light, $\sim 10^2$ GeV. If such a light mediator couples to charged leptons, it would already be strongly constrained by direct searches at colliders. In this work, we consider the scenario of a leptophobic light $Z'$ vector boson as the mediator, and study the the prospect of searching for it at the 8 TeV Large Hadron Collider (LHC). To improve the reach in the low mass region, we perform a detailed study of the processes that the $Z'$ is produced in association with jet, photon, $W^\pm$ and $Z^0$. We show that in the region where the mass of $Z'$ is between 80 and 400 GeV, the constraint from associated production can be comparable or even stronger than the known monojet and dijet constraints. Searches in these channels can be complementary to the monojet search, in particular if the $Z'$ couplings to quarks ($g_{Z'}$) and dark matter ($g_D$) are different. For $g_D < g_{Z'}$, we show that there is a larger region of parameter space which has correct thermal relic abundance and a light $Z'$, $M_{Z'} \sim 100 $ GeV. This region, which cannot be covered by the mono-jet search, can be covered by the resonance searches described in this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".