Neutrinos in Stochastic Media: From Sun to Core-Collapse
Bibliographic record
Abstract
Abstract. Recent work on neutrino propagation in stochastic media and its implications for the Sun and corecollapse supernovae are reviewed. It is shown that recent results from Sudbury Neutrino Observatory and SuperKamiokande combined with a best global fit value of δm 2 = 5 × 10 −5 eV 2 and tan 2 θ = 0.3 rule out solar electron density fluctuations of a few percent or more. It is argued that solar neutrino experiments may be able to rule out even smaller fluctuations in the near future. Recent observation of the charged-current solar neutrino flux at the Sudbury Neutrino Observatory [1] together with the measurements of the ν⊙-electron elastic scattering at the SuperKamiokande detector [2] established that there are at least two active flavors of neutrinos of solar origin reaching Earth. Analyses of all the solar neutrino data updated after the Sudbury Neutrino Observatory results were announced indicate (in two-flavor mixing schemes) a best fit value of δm 2 = 5 × 10 −5 eV 2 and tan 2 θ = 0.3 [3, 4]. In calculating neutrino survival probability in matter one typically assumes that the electron density of the Sun is a monotonically decreasing function of the distance from the core and ignores potentially de-cohering effects [5]. To understand such effects one possibility is to study parametric changes in the density [6, 7] or the role of matter currents [8]. Loreti and Balantekin [9] considered neutrino propagation in stochastic media. They studied the situation where the electron density in the medium has two components, one average component given by the Standard Solar Model or Supernova Model, etc., Ne(r), and one fluctuating component, Nr e (r). The two-flavor Hamiltonian describing neutrino propagation in such a medium is given by
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".