Data-Driven Reconstruction of a Low-Order Dynamo Model from Sunspot Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Understanding the long-term variability of the solar dynamo remains a key challenge in solar physics. In this work, we apply the Sparse Identification of Nonlinear Dynamical Systems (SINDy) framework to reconstruct a low-order dynamo model directly from 275 years of sunspot number data. Our data-driven approach for discovering governing equations from time series enables us to identify a minimal yet accurate dynamical system that captures the essential features of solar activity cycles. We demonstrate that, when interpreted as a low-order dynamo model, the solar dynamo is governed by an unstable saddle point, with nonlinear evolution leading to cyclic behavior. In particular we find that the underlying dynamics is described by a cubic nonlinearity driven by a $B_{\phi }\dot{B}_{\phi }^{2}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>B</mml:mi> <mml:mi>ϕ</mml:mi> </mml:msub> <mml:msubsup> <mml:mover> <mml:mi>B</mml:mi> <mml:mo>˙</mml:mo> </mml:mover> <mml:mi>ϕ</mml:mi> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> term, which results in a phase space not necessarily of the Van der Pol universality class. Additionally, we show that higher-order nonlinearities are disfavored, and we discuss how to interpret our findings in terms of a mean-field dynamo model with a novel quenching term.
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.
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it