Monochromatic dark neutrinos and boosted dark matter in noble liquid direct detection
Bibliographic record
Abstract
If dark matter self-annihilates into neutrinos or a second component of (``boosted'') dark matter that is nucleophilic, the annihilation products may be detected with high rates via coherent nuclear scattering. A future multi-ten-tonne liquid xenon detector such as DARWIN, and a multi-hundred-tonne liquid argon detector, argo, would be sensitive to the flux of these particles in complementary ranges of 10--1000 MeV dark matter masses. We derive these sensitivities after accounting for atmospheric and diffuse supernova neutrino backgrounds, and realistic nuclear recoil acceptances. We find that their constraints on the dark neutrino flux may surpass neutrino detectors such as Super-Kamiokande, and that they would extensively probe parametric regions that explain the missing satellites problem in neutrino portal models. The XENON1T and Borexino experiments currently restrict the effective baryonic coupling of thermal boosted dark matter to $\ensuremath{\lesssim}10--100\ifmmode\times\else\texttimes\fi{}$ the weak interaction, but darwin and argo would probe down to couplings 10 times smaller. Detection of boosted dark matter with baryonic couplings $\ensuremath{\sim}{10}^{\ensuremath{-}3}--{10}^{\ensuremath{-}2}\ifmmode\times\else\texttimes\fi{}$ the weak coupling could indicate that the dark matter density profile in the centers of galactic halos become cored, rather than cuspy, through annihilations. This work demonstrates that, alongside liquid xenon, liquid argon direct detection technology would emerge a major player in dark matter searches within and beyond the wimp paradigm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".