Neutrino flux sensitivity to the next galactic core-collapse supernova in COSINUS
Bibliographic record
Abstract
Abstract While neutrinos are often treated as a background for many dark matter experiments, these particles offer a new avenue for physics: the detection of core-collapse supernovae. Supernovae are extremely energetic, violent and complex events that mark the death of massive stars. During their collapse stars emit a large number of neutrinos in a short burst. These neutrinos carry 99% of the emitted energy which makes their detection fundamental in understanding supernovae. This paper illustrates how COSINUS (Cryogenic Observatory for SIgnatures seen in Next-generation Underground Searches), a sodium iodide (NaI) based dark matter search, will be sensitive to the next galactic core-collapse supernova. The experiment is composed of two separate detectors which will respond to far away and nearby supernovae. The inner core of the experiment will consist of NaI crystals operating as scintillating calorimeters. These crystals will mainly be sensitive to the Coherent Elastic Neutrino-Nucleus Scattering (CEνNS) against Na and I nuclei. The low mass of the cryogenic detectors enables the experiment to identify close supernovae within 1 kpc without pileup. The crystals will see up to hundreds of CEνNS events from a supernova happening at 200 pc. They reside at the center of a large cylindrical 230 T water tank, instrumented with 30 photomultiplier tubes. This tank acts simultaneously as a passive and active shield able to detect the Cherenkov radiation induced by impinging charged particles from ambient and cosmogenic radioactivity. A supernova near the Milky Way Center (10 kpc) will be easily detected inducing ∼60 measurable events, and the water tank will have a 3σ sensitivity to supernovae up to 22 kpc, seeing ∼10 events. This paper shows how, even without dedicated optimization, modern dark matter experiments will also be able to play their part in the multi-messenger effort to detect the next galactic core-collapse supernova.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".