The $B$ Anomalies, the $U_1$ Leptoquark and Dark Matter
Bibliographic record
Abstract
The present-day $B$-anomalies involving $b \to s \mu^+ \mu^-$ or $b \to c \tau^- {\bar\nu}$ transitions can all be explained with the addition of a vector $U_1$ leptoquark with a mass of $M_{U_1} \ge 1.8$ TeV. In the scalar singlet dark matter model (SSDMM), the DM is a scalar $S$ that couples to the Higgs via $\lambda_{hS} \, S^2|H|^2$. We update the fit to the data and find that the SSDMM is now viable only for $M_S \ge 1.6$ TeV. In this paper, we assume that the DM also couples to the $U_1$ via $\lambda_{U_1 S} \, S^2 \, U_{1\mu}^{\dagger} U^{\mu}_1$. In addition to leading to DM annihilation via $S S \to U_1 {\bar U}_1$, this coupling generates $SSgg$ and $SS\gamma\gamma$ couplings at one loop. Although naively divergent, these loop diagrams can be calculated under the assumption that the $U_1$ is a gauge boson of a group broken at the TeV scale. With this DM-$U_1$ coupling term, there are additional contributions to the various DM observables (relic density, direct and indirect detection). We find that the constraints on the SSDMM are relaxed for both heavy DM ($M_S \ge M_{U_1}$) and light DM ($M_S < M_{U_1}$).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".