Breaking up the Proton: An Affair with Dark Forces
Bibliographic record
Abstract
Deep inelastic scattering of ${e}^{\ifmmode\pm\else\textpm\fi{}}$ off protons is sensitive to contributions from ``dark photon'' exchange. Using HERA data fit to HERA's parton distribution functions (PDFs), we obtain the model-independent bound $\ensuremath{\epsilon}\ensuremath{\lesssim}0.02$ on the kinetic mixing between hypercharge and the dark photon for dark photon masses $\ensuremath{\lesssim}10\text{ }\mathrm{GeV}$. This slightly improves on the bound obtained from electroweak precision observables. For higher masses, the limit weakens monotonically; $\ensuremath{\epsilon}\ensuremath{\lesssim}1$ for a dark photon mass of 5 TeV. Utilizing PDF sum rules, we demonstrate that the effects of the dark photon cannot be (trivially) absorbed into refit PDFs and, in fact, lead to non--Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (Bjorken ${x}_{B}$-independent) scaling violations that could provide a smoking gun in data. The proposed ${e}^{\ifmmode\pm\else\textpm\fi{}}p$ collider operating at $\sqrt{s}=1.3\text{ }\text{ }\mathrm{TeV}$ (Large Hadron Electron Collider) is anticipated to accumulate ${10}^{3}$ times the luminosity of HERA, providing substantial improvements in probing the effects of a dark photon: sensitivity to $\ensuremath{\epsilon}$ well below that probed by electroweak precision data is possible throughout virtually the entire dark photon mass range, as well as being able to probe to much higher dark photon masses, up to 100 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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".