A Matched Filter Technique for Slow Radio Transient Detection and First Demonstration with the Murchison Widefield Array
Bibliographic record
Abstract
Abstract Many astronomical sources produce transient phenomena at radio frequencies, but the transient sky at low frequencies (<300 MHz) remains relatively unexplored. Blind surveys with new wide-field radio instruments are setting increasingly stringent limits on the transient surface density on various timescales. Although many of these instruments are limited by classical confusion noise from an ensemble of faint, unresolved sources, one can in principle detect transients below the classical confusion limit to the extent that the classical confusion noise is independent of time. We develop a technique for detecting radio transients that is based on temporal matched filters applied directly to time series of images, rather than relying on source-finding algorithms applied to individual images. This technique has well-defined statistical properties and is applicable to variable and transient searches for both confusion-limited and non-confusion-limited instruments. Using the Murchison Widefield Array as an example, we demonstrate that the technique works well on real data despite the presence of classical confusion noise, sidelobe confusion noise, and other systematic errors. We searched for transients lasting between 2 minutes and 3 months. We found no transients and set improved upper limits on the transient surface density at 182 MHz for flux densities between ∼20 and 200 mJy, providing the best limits to date for hour- and month-long transients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".