Optimization of Positioning of Interferometric Array Antennas Using Division Algorithm for Radio Astronomy Applications
Bibliographic record
Abstract
Abstract The Square Kilometre Array (SKA) ushers in the new generation of large radio telescopes that will work at wavelengths between meters and centimeters. In order to competitively design interferometric antenna arrays such as SKA, it is crucial to focus on the optimization of system performance. In this paper, we contribute to the solution by introducing a new optimization algorithm called Division Algorithm (DA). This algorithm finds the optimal positions of antennas to simultaneously maximize u–v coverage and decrease sidelobe level (SLL). The DA is able to optimize the configuration of the interferometric array in both snapshot and Earth rotation synthesis observations. To demonstrate its efficiency, the DA is applied to configure an optimum 30-element array for the Giant Metrewave Radio Telescope. The proposed algorithm is able to improve the overlapped samples parameter by about 4% and the unsampled cells parameter by about 12%, at snapshot observation, compared to the Genetic Algorithm (GA). DA is able to improve these two parameters for a 6-hr tracking observation as well. Finally, the proposed algorithm is compared with the GA for different source declination. Results show that the DA is able to decrease the SLL better than the GA.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".