Using the Antenna Impedance to Estimate Soil Electrical Parameters for the MIST Global 21 cm Experiment
Bibliographic record
Abstract
Abstract Radio experiments trying to detect the global 21 cm signal from the early Universe are very sensitive to the electrical properties of their environment. For ground-based experiments with the antenna above the soil it is critical to characterize the effect from the soil on the sky observations. This characterization requires estimating the soil’s electrical conductivity and relative permittivity in the same frequency range as the observations. Here we present our initial effort to estimate the conductivity and relative permittivity of the soil using the impedance of an antenna mounted at a distance above the surface. In this technique, the antenna used for soil characterization is the same as the antenna used for sky observations. To demonstrate the technique we use the antenna of the MIST global 21 cm experiment. We measured the antenna impedance at three sites in the Greater Concepción area, Chile. The measurements were done between 25 and 125 MHz, matching the range used by MIST for sky observations. The soil parameters were estimated by fitting the impedance measurements with electromagnetic simulations of the antenna and soil. In this initial effort the soil was modeled as homogeneous. The conductivity at the three sites was found to be between 0.007 and 0.049 Sm−1, and the relative permittivity between 1.6 and 12.7. The percent precision of the estimates at 68% probability is, with one exception, better (lower) than 33%. The best-fit simulations have a better than 10% agreement with the measurements relative to the peak values of the resistance and reactance across our frequency range. For MIST, these results represent a successful proof of concept of the use of the antenna impedance for soil characterization, and are expected to significantly improve in future implementations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".