Neutrino oscillation and expected event rate of supernova neutrinos in the adiabatic explosion model
Bibliographic record
Abstract
We study how the influence of the shock wave appears in neutrino oscillations and the neutrino spectrum by using the density profile of the adiabatic explosion model of a core-collapse supernova, which is calculated in an implicit Lagrangian code for general relativistic spherical hydrodynamics. We calculate expected event rates of neutrino detection at Super-Kamiokande (SK) and Sudbury Neutrino Observatory (SNO) for various ${\ensuremath{\theta}}_{13}$ values and both normal and inverted hierarchies. The predicted event rates of ${\overline{\ensuremath{\nu}}}_{e}$ and ${\ensuremath{\nu}}_{e}$ depend on the mixing angle ${\ensuremath{\theta}}_{13}$ for the inverted and normal mass hierarchies, respectively, and the influence of the shock wave appears for about 2--8 s when ${sin}^{2}2{\ensuremath{\theta}}_{13}$ is larger than ${10}^{\ensuremath{-}3}$. These neutrino signals for the shock-wave propagation is decreased by $\ensuremath{\lesssim}30%$ for ${\overline{\ensuremath{\nu}}}_{e}$ in inverted hierarchy (SK) or by $\ensuremath{\lesssim}15%$ for ${\ensuremath{\nu}}_{e}$ in normal hierarchy (SNO) compared with the case without shock. The obtained ratio of the total event for high-energy neutrinos ($20\text{ }\text{ }\mathrm{MeV}\ensuremath{\leqq}{E}_{\ensuremath{\nu}}\ensuremath{\leqq}60\text{ }\text{ }\mathrm{MeV}$) to low-energy neutrinos ($5\text{ }\text{ }\mathrm{MeV}\ensuremath{\lesssim}{E}_{\ensuremath{\nu}}\ensuremath{\leqq}20\text{ }\text{ }\mathrm{MeV}$) is consistent with the previous studies in schematic semianalytic or other hydrodynamic models of the shock propagation. The time dependence of the calculated ratio of the event rates of high-energy neutrinos to the event rates of low-energy neutrinos is a very useful observable which is sensitive to ${\ensuremath{\theta}}_{13}$ and mass hierarchies. Namely, the time-dependent ratio shows a clearer signal of the shock-wave propagation that exhibits a remarkable decrease by at most a factor of $\ensuremath{\sim}2$ for ${\overline{\ensuremath{\nu}}}_{e}$ in inverted hierarchy (SK), whereas it exhibits a smaller change by $\ensuremath{\sim}10%$ for ${\ensuremath{\nu}}_{e}$ in normal hierarchy (SNO). Observing the time-dependent high-energy to low-energy ratio of the neutrino events thus would provide a piece of very useful information to constrain ${\ensuremath{\theta}}_{13}$ and mass hierarchy and eventually help understand how the shock wave propagates inside the star.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 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".