Bibliographic record
Abstract
In the final stages of a massive star’s life, its core collapses under gravity, triggering a giant explosion called a core-collapse supernova. About 99% of the released energy emerges as neutrinos, which escape within seconds and precede the light signal by hours. Detecting these neutrinos provides both an early warning and a probe of stellar collapse. Since galactic supernovae are rare—only a few per century—it is important to maximize the number of sensitive detectors. This work investigates whether the Super Cryogenic Dark Matter Search (SuperCDMS) experiment, located 2 km underground at SNOLAB in Sudbury, Canada, could detect such bursts. Although designed for dark matter, its cryogenic Ge and Si detectors reach sub-keV thresholds, enabling sensitivity to supernova neutrinos via coherent elastic neutrino–nucleus scattering (CEνNS), a neutral-current process enhanced in heavy nuclei. Using neutrino fluence spectra from SN1987A and a 28 M⊙ model, I estimate CEνNS recoil spectra peaking below 1 keV, within SuperCDMS HV-mode reach. Expected yields are tens of events per Si detector and hundreds per Ge detector, concentrated in a few-second burst. Neutrino interactions in the ∼108.5 tonnes of lead shielding could produce secondary neutrons, but even under conservative efficiency assumptions, this background contributes only ≲20 detectable events for a 1 kpc supernova. These results show that SuperCDMS could provide a statistically significant CEνNS detection of a galactic supernova, making it a valuable dual-purpose experiment that supports both dark matter searches and supernova neutrino detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
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".