Inference of magnetic field during the Dalton minimum: Case study with recorded sunspot areas
Bibliographic record
Abstract
Abstract The Dalton minimum is considered to be one of the unique solar activity periods that have been captured in direct sunspot observations since 1610. Specifically, the solar magnetic field in this period is of great interest. Derfflinger and Prantner’s sunspot observations of 1802–1824 and 1800–1844 are the most important references for this period. To understand the solar magnetic activity in the Dalton minimum, it is important to estimate the latitude/longitude distribution of the sunspots and the sunspot areas for that duration. In this study, we analyze Derfflinger and Prantner’s sunspot drawings to determine the sunspot parameters, particularly the sunspot area. We find that the sunspot areas obtained from Derfflinger’s drawings are overemphasized by a factor of eight relative to those derived from modern observations. We also analyze Prantner’s sunspot drawings to validate our analysis of Derfflinger’s drawings. Further, we generate solar magnetograms from Derfflinger’s sunspot drawings using a deep-learning model based on conditional generative adversarial networks. Our analysis of these sunspot areas will provide important information for restoring the magnetograms during the Dalton minimum.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".