DAEδALUS and dark matter detection
Bibliographic record
Abstract
Among laboratory probes of dark matter, fixed-target neutrino experiments are particularly well suited to search for light weakly coupled dark sectors. In this paper, we show that the DAE\ensuremath{\delta}ALUS source setup---an 800 MeV proton beam impinging on a target of graphite and copper---can improve the present LSND bound on dark photon models by an order of magnitude over much of the accessible parameter space for light dark matter when paired with a suitable neutrino detector such as LENA. Interestingly, both DAE\ensuremath{\delta}ALUS and LSND are sensitive to dark matter produced from off-shell dark photons. We show for the first time that LSND can be competitive with searches for visible dark photon decays and that fixed-target experiments have sensitivity to a much larger range of heavy dark photon masses than previously thought. We review the mechanism for dark matter production and detection through a dark photon mediator, discuss the beam-off and beam-on backgrounds, and present the sensitivity in dark photon kinetic mixing for both the DAE\ensuremath{\delta}ALUS/LENA setup and LSND in both the on- and off-shell regimes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".