Presupernova neutrinos in large dark matter direct detection experiments
Bibliographic record
Abstract
The next Galactic core-collapse supernova (SN) is a highly anticipated observational target for neutrino telescopes. However, even prior to collapse, massive dying stars shine copiously in ``pre-supernova'' (pre-SN) neutrinos, which can potentially act as efficient SN warning alarms and provide novel information about the very last stages of stellar evolution. We explore the sensitivity to pre-SN neutrinos of large-scale direct dark matter detection experiments, which, unlike dedicated neutrino telescopes, take full advantage of coherent neutrino-nucleus scattering. We find that argon-based detectors with target masses of $\mathcal{O}(100)$ tons (i.e., comparable in size to the proposed ARGO experiment) operating at sub-keV thresholds can detect $\mathcal{O}(10--100)$ pre-SN neutrinos coming from a source at a characteristic distance of $\ensuremath{\sim}200\text{ }\text{ }\mathrm{pc}$, such as Betelgeuse ($\ensuremath{\alpha}$ Orionis). Large-scale xenon-based experiments with similarly low thresholds could also be sensitive to pre-SN neutrinos. For a Betelgeuse-type source, large-scale dark matter experiments could provide a SN warning siren $\ensuremath{\sim}10$ hours prior to the explosion. We also comment on the complementarity of large-scale direct dark matter detection experiments and neutrino telescopes in the understanding of core-collapse SN.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".