Searching for prompt and long-lived dark photons in electroproduced <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msup><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mo>−</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math> pairs with the heavy photon search experiment at JLab
Bibliographic record
Abstract
The heavy photon search experiment (HPS) at the Thomas Jefferson National Accelerator Facility searches for electroproduced dark photons. We report results from the 2016 engineering run consisting of $10\text{ }608\text{ }\text{ }{\mathrm{nb}}^{\ensuremath{-}1}$ of data for both the prompt and displaced vertex searches. A search for a prompt resonance in the ${e}^{+}{e}^{\ensuremath{-}}$ invariant mass distribution between 39 and 179 MeV showed no evidence of dark photons above the large QED background, limiting the coupling of ${\ensuremath{\epsilon}}^{2}\ensuremath{\gtrsim}{10}^{\ensuremath{-}5}$, in agreement with previous searches. The search for displaced vertices showed no evidence of excess signal over background in the masses between 60 and 150 MeV, but had insufficient luminosity to limit canonical heavy photon production. This is the first displaced vertex search result published by HPS. HPS has taken high-luminosity data runs in 2019 and 2021 that will explore new dark photon phase space.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.008 | 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".