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 ν e and ν x neutrinos, the detection of the ν e 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 ν e -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 ν e total emitted energy and mean energy accuracy, up to about 50%, if no other ν e -sensitive channel is implemented. A discussion on the reconstruction of ν e and ν x species, as well as the total emitted energy, is also presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".