Optimizing Kilonova Searches: A Case Study of the Type IIb SN 2025ulz in the Localization Volume of the Low-significance Gravitational Wave Event S250818k
Bibliographic record
Abstract
Abstract Kilonovae, the ultraviolet/optical/infrared counterparts to binary neutron star mergers, are an exceptionally rare class of transients. Optical follow-up campaigns are plagued by contaminating transients, which may mimic kilonovae but do not receive sufficient observations to measure the full photometric evolution. In this work, we present an analysis of the multiwavelength dataset of supernova (SN) 2025ulz, a proposed kilonova candidate following the low-significance detection of gravitational waves originating from the potential binary neutron star merger S250818k. Despite an early rapid decline in brightness, our multiwavelength observations of SN 2025ulz reveal that it is a type IIb SN. As part of this analysis, we demonstrate the capabilities of a novel quantitative scoring algorithm to determine the likelihood that a transient candidate is a kilonova, based primarily on its three-dimensional location and light-curve evolution. We also apply our scoring algorithm to other transient candidates in the localization volume of S250818k and find that, at all times after the discovery of SN 2025ulz, there are ≥4 candidates with a score comparable to SN 2025ulz, indicating that the kilonova search may have benefited from the additional follow-up of other candidates. During future kilonova searches, this type of scoring algorithm will be useful to rule out contaminating transients in real time, optimizing the use of valuable telescope resources.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".