A Model for the Total Solar Irradiance Based on Active Region Decay
Bibliographic record
Abstract
We present a model for the total solar irradiance. The model takes the observed location, timing, and area of emerging active regions as input and produces a time-evolving size distribution of magnetic structures over the solar surface. We assume that the bright magnetic structures (faculae), which counteract the irradiance deficit caused by sunspots, consist of the products of active region decay. We simulate the decay process as a combination of fragmentation and boundary erosion of large-scale magnetic structures. The model has several adjustable parameters that control the decay processes and the irradiance contribution from the quiet Sun and the small-scale magnetic elements that are produced during the decay process. We use a genetic algorithm to estimate these parameters by fitting to the observed irradiance and daily sunspot area time series over the 1978-2007 time interval. Given the simplifications associated with the model, the resultant parameter values are well constrained within the imposed ranges. In addition, the irradiance and daily sunspot area time series produced by the best-fit models agree very well with the observations, although the sunspot area fits tend to perform better than the irradiance fits. However, it is evident that the model is neglecting a significant source of excess brightness, which manifests itself in two ways. First, the optimal values for the lifetime and intensity contrast of the bright, small-scale flux elements are both larger than expected. Second, the synthetic irradiance consistently underestimates the observations during the ascending phase of a cycle, despite the daily sunspot area fitting the observations quite well during these times. We also show that this genetic forward modeling approach can be used to detect a long-term trend of decadal timescale in the quiet-Sun irradiance. Assuming a constant quiet-Sun irradiance, we reconstruct the total solar irradiance over the 1874-1978 time interval, for which observational data of emerging active regions are available.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".