Illuminating dark photons with high-energy colliders
Bibliographic record
Abstract
High-energy colliders offer a unique sensitivity to dark photons, the mediators of a broken dark U(1) gauge theory that kinetically mixes with the Standard Model (SM) hypercharge. Dark photons can be detected in the exotic decay of the 125 GeV Higgs boson, h→ZZ D →4ℓ, and in Drell-Yan events, pp→Z D → ℓℓ. If the dark U(1) is broken by a hidden-sector Higgs mechanism, then mixing between the dark and SM Higgs bosons also allows the exotic decay h → Z D Z D → 4ℓ. We show that the 14 TeV LHC and a 100 TeV proton-proton collider provide powerful probes of both exotic Higgs decay channels. In the case of kinetic mixing alone, direct Drell-Yan production offers the best sensitivity to Z D , and can probe ϵ ≳ 9 × 10−4 (4 × 10−4) at the HL-LHC (100 TeV pp collider). The exotic Higgs decay h → ZZ D offers slightly weaker sensitivity, but both measurements are necessary to distinguish the kinetically mixed dark photon from other scenarios. If Higgs mixing is also present, then the decay h → Z D Z D can allow sensitivity to the Z D for ϵ ≳ 10−9 − 10−6 (10−10 − 10−7) for the mass range $$ 2{m}_{\mu }<{m_Z}_{{}_D}<{m}_h/2 $$ by searching for displaced dark photon decays. We also compare the Z D sensitivity at pp colliders to the indirect, but model-independent, sensitivity of global fits to electroweak precision observables. We perform a global electroweak fit of the dark photon model, substantially updating previous work in the literature. Electroweak precision measurements at LEP, Tevatron, and the LHC exclude ϵ as low as 3 × 10−2. Sensitivity can be improved by up to a factor of ∼ 2 with HL-LHC data, and an additional factor of ∼ 4 with ILC/GigaZ data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".