A direction-dependent framework for visibility plane mosaicing and primary beam correction
Bibliographic record
Abstract
ABSTRACT With the increasing sensitivity of modern radio interferometers, it has become important to image objects larger than the field of view while optimizing sensitivity and image fidelity. We present a coherent visibility plane direction-dependent imaging, calibration, and mosaicing framework. Our simulations and application to real MeerKAT data show that this joint deconvolution and primary beam correction approach, coupled with direction-dependent calibration, allows for deeper mosaics with greater fidelity and increased accuracy of recovered flux densities and spectral indices, especially beyond the half-power beam width. Our best-case mosaic produces precise flux values within a 6 per cent uncertainty and spectral indices within 20 per cent throughout the imaged area, and is fully complete out to twice the radii and half the flux density than the image plane equivalent. The application to archival wide-band MeerKAT 1283 MHz data produces the deepest high-resolution image of the Shapley Supercluster Core, with a sensitivity of 3.6 $\mu$Jy beam−1 within the primary beam at a 7 arcsec resolution, constituting a $\sim$50 per cent increase in dynamic range over the image plane counterpart, and a fluxscale that is consistent within 10 per cent across the entire field of view. The compute time for the direction-dependent visibility plane mosaic was comparable to the sum of the times needed to perform direction-dependent calibration on the individual pointings. Our results suggest that visibility plane mosaicing with its capability for deeper deconvolution could improve the efficiency of deep and wide surveys, particularly for on-the-fly mapping and studies of low surface brightness sources, and could form the basis of future calibration pipelines for SKA-scale instruments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".