Identifying ULF Wave Eigenfrequencies in SuperDARN Backscatter Using a Lomb‐Scargle Cross‐Phase Analysis
Bibliographic record
Abstract
Abstract The eigenfrequencies of standing Alfvén waves on closed magnetospheric field lines can be estimated using the cross‐phase technique. These eigenfrequencies can be used to monitor the plasma mass density distribution along the field line. So far, this has only been applied to ground‐based magnetometer data. The Super Dual Auroral Radar Network (SuperDARN) radars offer some benefits over magnetometers. They provide greater spatial resolution and coverage, as well as direct sensing above the E region ionosphere, which screens ultralow frequency (ULF) waves from the ground. However, there are significant data quality issues. These include the uncertain origin of radar backscatter, uneven sampling of data due to data gaps, and inaccurate fitting to the autocorrelation functions. Artificial backscatter from an ionospheric heater has been used to remove the uncertainty in backscatter location. We have developed a Lomb‐Scargle cross‐phase analysis for application to discontinuous radar data. The First Principles Fitting Methodology has been used to improve the fitted data products derived from the autocorrelation functions. Using these techniques, we have shown that it is possible to measure eigenfrequencies with SuperDARN data, and we have verified an example using ground‐based magnetometer data. Finally, we have demonstrated that the eigenfrequency signature in this example was caused by a broadband source of energy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".