Strongly lensed candidates from the HSC transient survey
Bibliographic record
Abstract
We present a lensed quasar search based on the variability of lens systems in the Hyper Suprime-Cam (HSC) transient survey. Starting from 101 353 variable objects withi-band photometry in the HSC transient survey, we used a variability-based lens search method measuring the spatial extent in difference images to select potential lensed quasar candidates. We adopted conservative constraints in this variability selection and obtained 83 657 variable objects as possible lens candidates. We then ran CHITAH, a lens search algorithm based on the image configuration, on those 83 657 variable objects, and 2130 variable objects were identified as potential lensed objects. We visually inspected the 2130 variable objects, and seven of them are our final lensed quasar candidates. Additionally, we found one lensed galaxy candidate as a serendipitous discovery. Among the eight final lensed candidates, one is the only known quadruply lensed quasar in the survey field, HSCJ095921+020638. None of the other seven lensed candidates have been previously classified as a lens nor a lensed candidate. Three of the five final candidates with availableHubbleSpace Telescope (HST) images, including HSCJ095921+020638, show clues of a lensed feature in the HST images. We show that a tightening of our variability selection criteria might result in the loss of possible lensed quasar candidates, especially the lensed quasars with faint brightness or narrow separation, without efficiently eliminating the non-lensed objects; CHITAHis therefore important as an advanced examination to improve the lens search efficiency through the object configuration. The recovery of HSCJ095921+020638 proves the effectiveness of the variability-based lens search method, and this lens search method can be used in other cadenced imaging surveys, such as the upcomingRubinObservatory Legacy Survey of Space and Time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".