Quasar lenses in the south: searches over the DES public footprint
Bibliographic record
Abstract
ABSTRACT We have scanned 5000 deg2 of Southern Sky to search for strongly lensed quasars with five methods, all source oriented, but based on different assumptions and selection criteria. We present a list of high-grade candidates from each method (totalling 98 unique, new candidates), to facilitate follow-up spectroscopic campaigns, including two previously unknown quadruplets, WG 210014.9-445206.4 and WG 021416.37-210535.3. We analyse morphological searches based on Gaia multiplet detection and astrometric offsets, fibre-spectroscopic pre-selection, and X-ray and radio pre-selection. The performance and complementarity of the methods are evaluated on a common sample of known lenses in the Dark Energy Survey public Data Release 1 (DR1) footprint. We recovered in total 13 known lenses, of which 8 are quadruplets. Morphological and colour selection of objects, from the WISE andGaia-DR2 surveys, recovers most of those known lenses, but searches in the radio and X-ray cover regimes that are beyond the completeness of Gaia. Given the footprint, pre-selection, and depth limits, the current number of quads indicates that the union of these searches is complete, and the expected purity on high-grade candidates is ${\approx}60{{\ \rm per\ cent}}$. Ongoing, spectroscopic campaigns confirm this estimate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".