The International Liquid Mirror Telescope project : optical quality tests and prospective detection of multiply imaged quasars
Bibliographic record
Abstract
The International Liquid Mirror Telescope (ILMT) project is a joint collaborationbetween different universities and research institutes in Belgium, Canada, Indiaand Poland, for the design, construction and operation of a 4 meter liquid mirrortelescope at the Devasthal Observatory (India).In the framework of the present thesis, we have contributed to the developmentof the ILMT. We have namely designed and manufactured an innovative instru-ment capable of measuring the optical quality of the primary mirror that maybe affected by the propagation of wavelets on the mercury layer. The instrumentis composed of a laser source, emitting a beam whose reflection on the mirror ismodulated by slope variations induced by the wavelets. Preliminary tests werecarried out showing the validity of the method for on site testing of the mirror.The ILMT has been designed to perform a photometric variability survey ofa narrow strip of sky, making it very suitable for the detection and follow-up ofphotometrically variable sources such as supernovae and quasars.In the second part of this thesis, we present an estimate of the number of QSOsto be detected within the ILMT survey, and of the expected number of multiplyimaged sources among these caused by the presence of a deflector near the lines-of-sight. We have studied the impact of various parameters on the expected numberof detected gravitational lens systems, such as the instrumental resolution of thetelescope, the galaxy population type(s) and corresponding lensing model(s), andthe cosmological parameters.The statistical sample of multiply imaged QSOs is intended to be used asa cosmological probe. In order to make a sensitivity comparison between vari-ous modelling approaches, we introduce a new formalism to estimate the lensingprobabilities, based on the joined probability density of the observed QSOs.This new formalism allows to calculate three probability densities: that asso-ciated with the optical depth distribution, as well as those related to the deflector and the lensed source redshift distributions. For the case of FLRW universes, wecompare the sensitivity of these distributions as a function of the cosmologicalmass density.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".