A matched-filter approach to radio variability and transients: searching for orphan afterglows in the VAST Pilot Survey
Bibliographic record
Abstract
Radio transient searches using traditional variability metrics struggle to recover sources whose evolution timescale is significantly longer than the survey cadence. Motivated by the recent observations of slowly evolving radio afterglows at gigahertz frequency, we present the results of a search for radio variables and transients using an alternative matched-filter approach. We designed our matched-filter to recover sources with radio light curves that have a high-significance fit to power-law and smoothly broken power-law functions; light curves following these functions are characteristic of synchrotron transients, including "orphan" gamma-ray burst afterglows, which were the primary targets of our search. Applying this matched-filter approach to data from Variables and Slow Transients Pilot Survey conducted using the Australian SKA Pathfinder, we produced five candidates in our search. Subsequent Australia Telescope Compact Array observations and analysis revealed that: one is likely a synchrotron transient; one is likely a flaring active galactic nucleus, exhibiting a flat-to-steep spectral transition over $4\,$months; one is associated with a starburst galaxy, with the radio emission originating from either star formation or an underlying slowly evolving transient; and the remaining two are likely extrinsic variables caused by interstellar scintillation. The synchrotron transient, VAST J175036.1$-$181454, has a multi-frequency light curve, peak spectral luminosity, and volumetric rate that is consistent with both an off-axis afterglow and an off-axis tidal disruption event; interpreted as an off-axis afterglow would imply an average inverse beaming factor $\langle f^{-1}_{\text{b}} \rangle = 860^{+1980}_{-710}$, or equivalently, an average jet opening angle of $\langle θ_{\textrm{j}} \rangle = 3^{+4}_{-1}\,$deg.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".