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Record W4220901127 · doi:10.3389/fspas.2022.853676

External Forcing of the Solar Dynamo

2022· article· en· W4220901127 on OpenAlexafffund
Paul Charbonneau

Bibliographic record

VenueFrontiers in Astronomy and Space Sciences · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsDynamoPerturbation (astronomy)Forcing (mathematics)Solar dynamoAstrophysicsDynamo theoryClassical mechanicsMagnetic fieldAstronomyAtmospheric sciencesQuantum mechanics

Abstract

fetched live from OpenAlex

In this paper I examine whether external forcing of the solar dynamo on long timescales can produce detectable signal in the form of long term modulation of the magnetic cycle. This task is motivated in part by some recent proposals (Abreu et al., 2012; Astron. Ap. , 548, A88; Stefani et al., 2021; Solar Phys. , 296, 88), whereby modulation of the solar activity cycle on centennial and millennial timescales, as recovered from the cosmogenic radioisotope record, is attributed to perturbation of the tachocline driven by planetary orbital motions. Working with a two-dimensional mean-field-like kinematic dynamo model of the Babcock-Leighton variety, I show that such an external forcing signal may be detectable in principle but is likely to be obliterated by other internal sources of fluctuations, in particular stochastic perturbations of the dynamo associated with convective turbulence, unless a very efficient amplification mechanism is at play. I also examine the ability of external tidal forcing to synchronize an otherwise autonomous, internal dynamo operating at a nearby frequency. Synchronization is readily achieved, and turns out to be very robust to the introduction of stochastic noise, but requires very high forcing amplitudes, again highlighting the critical need for a powerful amplification mechanism.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.359

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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 designObservational
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

Citations20
Published2022
Admission routes2
Has abstractyes

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