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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".