A Measure of Well-Spread Points in Noise Wave-Based Source Matrix for Wideband Noise Parameter Measurement: The SKA-Low Example
Bibliographic record
Abstract
The existence of a figure of merit for measuring the degree of well-spread source points in noise parameter extraction has long been conjectured. This article proposes a measure based on noise waves that is physically motivated and is directly connected to linear algebra through the matrix condition number and/or determinant. The key to this figure of merit is the selection of the noise temperature equation and the removal of singularity due to the 1/(1-|Γ| <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) factor. The result is a well-scaled source matrix with entries bounded within a unit circle. We demonstrate the effectiveness of this measure by extracting the noise parameters of an amplifier in the low-frequency Square Kilometre Array (SKA-Low) band of 50-350 MHz using seven tuner positions. The noise parameters in the 50-100-MHz band are successfully measured despite being below the 100-MHz tuner rating. This outcome is very well predicted by the condition number and the determinant of the source matrix in question.
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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 | 0.001 | 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 | 0.000 | 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".