A comparison of SuperDARN ACF fitting methods
Bibliographic record
Abstract
The Super Dual Auroral Radar Network (SuperDARN) is a worldwide chain of HF radars which monitor plasma dynamics in the ionosphere. Autocorrelation functions are routinely calculated from the radar returns and applied to estimate Doppler velocity, spectral width, and backscatter power. This fitting has traditionally been performed by a routine called FITACF. This routine initiates a fitting by selecting a subset of valid phase measurements and then empirically adjusting for 2π phase ambiguities. The slope of the phase variation with lag time then provides Doppler velocity. Doppler spectral width is found by an independent fitting of the decay of power to an assumed exponential or Gaussian function. In this paper, we use simulated data to assess the performance of FITACF, as well as two other newer fitting techniques, named FITEX2 and LMFIT. The key new feature of FITEX2 is that phase models are compared in a least‐squares fitting sense with the actual data phases to determine the best fit, eliminating some ambiguities which are present in FITACF. The key new feature of LMFIT is that the complex autocorrelation function (ACF) itself is fit, and Doppler velocity, spectral width, and backscatter power are solved simultaneously. We discuss some of the issues that negatively impact FITACF and find that of the algorithms tested, LMFIT provides the best overall performance in fitting the SuperDARN ACFs. The techniques and the data simulator are applicable to other radar systems that utilize multipulse sequences to make simultaneous range and velocity determinations under aliasing conditions.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".