Charting WIMP territories at the neutrino floor
Bibliographic record
Abstract
We establish comprehensive theoretical benchmarks for weakly interacting massive particles (WIMPs) accessible to ultimate direct detection experiments, focusing on the challenging parameter space between current experimental limits and the irreducible neutrino background. We systematically examine both thermal freeze-out and freeze-in production mechanisms across a range of simplified dark matter models, including s -channel scalar and vector portals, t -channel mediator scenarios, and electroweakly interacting multiplets. For thermal relics, we identify parameter regions where suppressed direct detection cross sections naturally arise through momentum-dependent interactions and blind-spot configurations, while maintaining the correct relic abundance. We extensively investigate freeze-in scenarios, demonstrating how feebly interacting massive particles in portal models can populate experimentally accessible parameter space despite their ultraweak couplings. Additionally, we explore how nonstandard cosmological histories—including early matter domination and fast-expanding Universe scenarios—can dramatically alter the relationship between relic density and detection prospects, opening new avenues for discovery. Our analysis provides a roadmap for next-generation experiments approaching the neutrino floor, highlighting complementary detection strategies and identifying the most promising theoretical targets for ultimate sensitivity dark matter searches. These benchmarks establish the theoretical foundation for the final push toward comprehensive coverage of well-motivated WIMP parameter 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".