Concentric Circles and Spiral Configurations for Large Correlator Arrays in Radio Astronomy
Bibliographic record
Abstract
Abstract Aperture synthesis arrays are commonly used in radio astronomy to take images of radio point sources, with the planned Square Kilometre Array (SKA) being the most common example. One approach to enhancing the quality of the images is to optimize an antenna array configuration in a possible SKA implementation. An ideal arrangement must ensure optimal configurations to capture a clear image by either decreasing the sidelobe level (SLL) in the l–m domain or increasing the sampled data in the spatial-frequency domain. In this paper a novel configuration is considered to optimize the array by considering all possible observation situations through the positions of the antenna array elements via a mathematical model that we call geometrical method (GM). To demonstrate its efficiency, the technique is applied to developing an optimal configuration for the elements of the Giant Metrewave Radio Telescope (GMRT). The effect of these changes, particularly in the forms of circular and spiral arrangements, is discussed. It is found that a spiral configuration results in fewer overlapping samples than the number of antennas placed along three arms of the GMRT with fewer than 11% and 27% overlapping samples in the snapshot and 6 hr tracking observations, respectively. Finally, the spiral configuration reduces the first SLL from −13.01 dB, using the arms of the current GMRT configuration, to −15.64 dB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".