Dark Photon and Muon <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>g</mml:mi><mml:mo>−</mml:mo><mml:mn>2</mml:mn></mml:math> Inspired Inelastic Dark Matter Models at the High-Energy Intensity Frontier
Bibliographic record
Abstract
We study hidden-sector particles at past (CERN-Hamburg-Amsterdam-Rome-Moscow Collaboration and NuCal), present (NA62, SeaQuest, and DarkQuest), and future (LongQuest) experiments at the high-energy intensity frontier. We focus on exploring the minimal vector portal and the next-to-minimal models in which the productions and decays are decoupled. These next-to-minimal models have mostly been devised to explain experimental anomalies while avoiding existing constraints. We demonstrate that proton fixed-target experiments provide one of the most powerful probes for the MeV to few GeV mass range of these models, using inelastic dark matter (iDM) as an example. We consider an iDM model with a small mass splitting that yields the observed dark matter relic abundance, and a scenario with a sizable mass splitting that can also explain the muon g-2 anomaly. We set strong limits based on the CERN-Hamburg-Amsterdam-Rome-Moscow Collaboration and NuCal experiments, which come close to excluding iDM as a full-abundance thermal dark matter candidate in the MeV to GeV mass range. We also make projections based on NA62, SeaQuest, and DarkQuest and update the constraints of the minimal dark photon parameter space. We find that NuCal sets the only existing constraint in ε∼10^{-8}-10^{-4} regime, reaching ∼800 MeV in dark photon mass due to the resonant enhancement of proton bremsstrahlung production. These studies also motivate LongQuest, a three-stage retooling of the SeaQuest experiment with short (≲5 m), medium (∼5 m), and long (≳35 m) baseline tracking stations and detectors as a multipurpose machine to explore new physics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.009 | 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".