Expansion of a Y-Shaped Antenna Array and Optimization of the Future Antenna Array in Malaysia for Astronomical Applications
Bibliographic record
Abstract
To achieve high quality images from the sky by extending an existing interferometric array, in this work, the Geometrical Method (GM), Genetic Algorithm (GA), and Division Algorithm (DA) are compared. These methods are each applied independently to an interferometer array starting from the same initial conditions. Using the GM method, the spiral configuration is suggested as an optimum arrangement that provides the desired u-v coverage with low side lobe levels (SLLs). Using the GA method, as the number of generations is increased, the unsampled cells are reduced, enhancing the imaging quality. As such, the algorithm improves the overlapped samples as it works with a greater number of generations. Moreover, the GA is able to suppress the SLL. Finally, the DA is applied to such an array. Results show that the DA is able to process the sampled data with less overlapping of the data in the snapshot observations, in comparison to the other discussed configurations in this paper; effectively the DA reduces the overlapped samples, such that it is more efficient than the GA. The configuration of antennas that arrives by applying the DA method can achieve a certain image quality with less overlapping, as compared to the configuration arriving by applying the GA method. The calculated SLLs for the DA configuration are used to demonstrate that the efficiency of the DA is potentially better than that of the GA. Moreover, the GA and DA algorithms discussed in this study are applied to an array of 10 antennas with coordinates that represent the antennas deployed in Malaysia. Results show that the DA can reduce the overlapping of the samples more efficiently than the GA for a 6-hour tracking observation and in terms of unsampled cells the DA has the same efficiency of the GA.
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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 | 0.000 |
| 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".