Direct detection constraints on dark photon dark matter
Bibliographic record
Abstract
Dark matter detectors built primarily to probe elastic scattering of WIMPs on nuclei are also precise probes of light, weakly coupled, particles that may be absorbed by the detector material. In this paper, we derive constraints on the minimal model of dark matter comprised of long-lived vector states V (dark photons) in the 0.01– 100 keV mass range. The absence of an ionization signal in direct detection experiments such as XENON10 and XENON100 places a very strong constraint on the dark photon mixing angle, down to O ( 10 − 15 ) , assuming that dark photons comprise the dominant fraction of dark matter. This sensitivity to dark photon dark matter exceeds the indirect bounds derived from stellar energy loss considerations over a significant fraction of the available mass range. We also revisit indirect constraints from V → 3 γ decay and show that limits from modifications to the cosmological ionization history are comparable to the updated limits from the diffuse γ -ray flux.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".