PSJ2107-1611: a new wide-separation, quadruply imaged lensed quasar with flux ratio anomalies
Bibliographic record
Abstract
We report the discovery of PSJ2107-1611, a fold-configuration 4.3"-separation quadruply lensed quasar with a bright lensed arc. It was discovered using a convolutional neural network on Pan-STARRS gri images of pre-selected quasar candidates with multiple nearby Pan-STARRS detections. Spectroscopic follow-up with EFOSC2 on the ESO 3.58m New Technology Telescope reveals the source to be a quasar at z = 2.673, with the blended fold image pair showing deformed broad lines relative to the other images. The flux ratios measured from optical to near-infrared imaging in the Canada-France-Hawaii Telescope Legacy Survey, Pan-STARRS, the Legacy Surveys, and the Vista Hemisphere Survey are inconsistent with a smooth mass model as the fold pair images are about 15 times too faint. Variability, time delay effects, and reddening are ruled out through multiple-epoch imaging and color information. The system is marginally resolved in the radio in the Very Large Array Sky Survey S-band, where it has a 10 mJy detection. The radio flux ratios are compatible with the smooth mass macromodel. This system offers a unique tool for future studies of quasar structure with strong and microlensing. A more detailed analysis of follow-up with JWST/MIRI, VLT/MUSE, VLT/ERIS, and data from the European Very Long Baseline Interferometer will be presented in an upcoming paper.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".