Supernova neutrino fluxes in HALO-1kT,Super-Kamiokande, and JUNO
Bibliographic record
Abstract
Abstract When the next galactic core-collapse supernova occurs, we must be ready to obtain as much information as possible. Although many present and future detectors are well equipped to detect νe and νx neutrinos, the detection of the νe species presents the biggest challenges. We assess the impact that a 1 ktonne lead-based detector, such as HALO-1kT, can have in constraining electron neutrino time-integrated fluxes. The study involves the detector taken alone as well as when combined with massive νe-sensitive detectors such as Super-Kamiokande and JUNO. We find that HALO-1kT alone is not able to strongly constrain the emission parameters. When combined with other detectors, however, the orthogonal information might be helpful in improving the νe total emitted energy and mean energy accuracy, up to about 50%, if no other νe-sensitive channel is implemented. A discussion on the reconstruction of νe and νx species, as well as the total emitted energy, is also presented.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".