Observation of Multiply Imaged Quasars with the 4-m ILMT
Bibliographic record
Abstract
Gravitationally lensed quasars (GLQs) are known to potentially provide an independent way of determining the value of the Hubble-Lemaître parameter H0, to probe the dark matter content of lensing galaxies and to resolve tiny structures in distant active galactic nuclei. That is why multiply imaged quasars are one of the main drivers for a photometric monitoring with the 4-m International Liquid Mirror Telescope (ILMT). We would like to answer the following questions: how many multiply imaged quasars should we be able to detect with the ILMT? And how to derive accurate magnitudes of the GLQ images? Our estimation of the possible number of multiply imaged quasars is 15, although optimistic forecasts predict up to 50 of them. We propose to use the adaptive PSF fitting method for accurate flux measurements of the lensed images. During preliminary observations in spring 2022 we were able to detect the quadruply imaged quasar SDSS J1251+2935 in the i’ and r’ spectral bands.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".