Foraging for dark matter in large volume liquid scintillator neutrino detectors with multiscatter events
Bibliographic record
Abstract
We show that dark matter with a per-nucleon scattering cross section $\ensuremath{\gtrsim}{10}^{\ensuremath{-}28}\text{ }\text{ }{\mathrm{cm}}^{2}$ could be discovered by liquid scintillator neutrino detectors like Borexino, $\mathrm{SNO}+$, and JUNO. Due to the large dark matter fluxes admitted, these detectors could find dark matter with masses up to $1{0}^{21}\text{ }\text{ }\mathrm{GeV}$, surpassing the mass sensitivity of current direct detection experiments (such as XENON1T and PICO) by over 2 orders of magnitude. We derive the spin-independent and spin-dependent cross section sensitivity of these detectors using existing selection triggers, and we propose an improved trigger program that enhances this sensitivity by 2 orders of magnitude. We interpret these sensitivities in terms of three dark matter scenarios: (1) effective contact operators for scattering, (2) QCD-charged dark matter, and (3) a recently proposed model of Planck-mass baryon-charged dark matter. We calculate the flux attenuation of dark matter at these detectors due to the earth overburden, taking into account the earth's density profile and elemental composition, as well as nuclear spins.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".