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Record W2070297124 · doi:10.1134/1.1772450

MHD origin of density fluctuations deep within the Sun and their influence on neutrino oscillation parameters in LMA MSW scenario

2004· article· en· W2070297124 on OpenAlexaff
V. B. Semikoz, C. P. Burgess, N. S. Dzhalilov, T. I. Rashba, J. W. F. Valle

Bibliographic record

VenuePhysics of Atomic Nuclei · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysicsHelioseismologySolar coreOscillation (cell signaling)NeutrinoMagnetohydrodynamicsNeutrino oscillationAstrophysicsComputational physicsRadiative transferSolar neutrinoSolar neutrino problemMagnetic fieldOpticsParticle physicsQuantum mechanics

Abstract

fetched live from OpenAlex

We analyze helioseismic waves near the solar equator in the presence of magnetic fields deep within the solar radiative zone. We find that reasonable magnetic fields can significantly alter the shapes of the wave profiles for helioseismic g modes. They can do so because the existence of density gradients allows g modes to resonantly excite Alfvén waves, causing mode energy to be funneled along magnetic field lines, away from the solar equatorial plane. The resulting waveforms show comparatively sharp spikes in the density profiles at radii where these resonances take place. Such matter density waves with known spatial structure are substituted as a matter density noise into the 2×2 Schrödinger equation for ν e,π neutrinos oscillating within the Sun. Then we reexamine the sensitivity of solar neutrino oscillations to noise in the solar interior using the best current estimates of neutrino properties. Our results show that the measurement of neutrino properties at KamLAND provides new information about fluctuations in the solar environment on scales to which standard helioseismology constraints are largely insensitive. We also show how the determination of neutrino oscillation parameters from a combined fit of KamLAND and solar data depends strongly on the magnitude of solar density fluctuations.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.269
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2004
Admission routes1
Has abstractyes

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