Bibliographic record
Abstract
Abstract This Letter proposes a new search for confining dark sectors at the Large Hadron Collider. As a result of the strong dynamics in the hidden sector, dark matter could manifest in proton-proton collisions at the Large Hadron Collider in form of hadronic jets containing stable invisible bound states. These semi-visible jets have been studied theoretically and experimentally in the fully hadronic signature where the unstable composite dark matter can only decay promptly back to Standard Model quarks. We present a simplified model based on two messenger fields separated by a large mass gap allowing dark bound states to decay into pairs of oppositely charged leptons. The resulting experimental signature is characterized by non-isolated lepton pairs inside semi-visible jets. We propose a search strategy independent from the underlying model assumptions targeting this new signature, and discuss the orthogonality with respect to the existing searches. Remaining agnostic on the shape of the di-lepton spectrum, we determine the sensitivity of a dedicated analysis to the target signal. The proposed search can claim the $$3 \sigma $$ 3 σ evidence (exclusion) of the heavier mediator up to masses of 3.5 TeV (4.5 TeV) with the full Run 2 data of the LHC. Exploiting the resonant feature of the lepton pairs can enhance the sensitivity reach on a specific model. We estimate that an analysis using the di-lepton invariant mass information can reach $$5 \sigma $$ 5 σ discovery up to masses of 3.5 TeV and improve the exclusion up to more than 5 TeV.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".