Determining the Lensing Fraction of SDSS Quasars: Methods and Results from the Early Data Release
Bibliographic record
Abstract
We present an algorithm for selecting gravitational lens candidates from amongst Sloan Digital Sky Survey (SDSS) quasars. In median Early Data Release (EDR) conditions, the algorithm allows for the recovery of pairs of equal flux point sources down to separations of $\sim 0{\farcs}7$ or with flux ratios up to $\sim$ 10:1 at a separation of $1\farcs5$. The algorithm also recovers a wide variety of plausible quad geometries. We also present a method for determining the selection function of this algorithm through the use of simulated SDSS images and introduce a method for calibrating our simulated images through truth-testing with real SDSS data. Finally, we apply our algorithm and selection function to SDSS quasars from the EDR to get an upper bound on the lensing fraction. We find 13 candidates among 5120 z $>$ 0.6 SDSS quasars, implying an observed lensing fraction of not more than 4 $\times 10^{-3}$. There is one likely lens system in our final sample, implying an observed lensing fraction of not less than $3 \times 10^{-5}$ (95% confidence levels).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".