A new method for short-duration transient detection in radio images: searching for transient sources in MeerKAT data of NGC 5068
Bibliographic record
Abstract
ABSTRACT Transient surveys are a vital tool in exploring the dynamic Universe, with radio transients acting as beacons for explosive and highly energetic astrophysical phenomena. However, performing commensal transient surveys using radio imaging can require a significant amount of computing power, data storage, and time. With the instrumentation available to us, and with new and exciting radio interferometers in development, it is essential that we develop efficient methods to probe the radio transient sky. In this paper, we present results from a commensal short-duration transient survey, on time-scales of 8 s, 128 s, and 1 h, using data from the MeerKAT radio telescope. The data set used was obtained as part of a galaxy observing campaign, and we focus on the field of NGC 5068. We present a quick, wide-field imaging strategy to enable fast imaging of large data sets, and develop methods to efficiently filter detected transient candidates. No transient candidates were identified on the time-scales of 8 s, 128 s, and 1 h, leading to competitive limits on the transient surface densities of $6.7\, {\times }\, 10^{-5}$, $1.1\, {\times }\, 10^{-3}$, and $3.2\, {\times }\, 10^{-2}$ deg−1 at sensitivities of 56.4, 19.2, and 3.9 mJy following primary beam correction for the respective time-scales. We find one possible candidate that could be associated with a stellar flare, which was rejected due to strict image quality control. Further short time-scale radio observations of this candidate could give definite results about its origin.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".