Automatic Identification of Hexagonal Pattern Artifacts in Radio Astronomical Surveys
Bibliographic record
Abstract
The Radio Astronomical Survey (RAS) is a collection of image cubes that represent specific regions in the sky. They are produced through an automated Data Processing Pipeline. In this process, radio astronomical signals received from a radio telescope are mapped to image cubes. Each image cube consists of thousands of two-dimensional images, each image represents the radio signals received by the telescope at a certain frequency. The final stage of the Pipeline process is to visually inspect the produced surveys to discover artifacts. In addition to being a time consuming process, the manual inspection is subjective which makes the validation process imperfect and non-uniform. One of the common artifacts in several RASs is the Hexagonal Pattern Artifact (HPA) which may happen due to telescope influence. Since these artifacts are undesirable, they should be removed or flagged before releasing the RAS to the community. This research presents the first algorithm for Automatic Identification of HPA in RAS (AIHR). The automation process will dramatically reduce the amount of time and effort invested to manually discover HPAs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".