Necessity of a TDI Optical Corrector for ILMT Observations
Bibliographic record
Abstract
The International Liquid Mirror Telescope (ILMT) has recently become operational at the Devasthal Observatory of ARIES, Nainital, India. The ILMT observes in the Time delay integration (TDI) mode where the images are formed by electronically stepping the charges over the pixels of the CCD, along a column. Observations near the zenith impose certain constraints dependent on the latitude such as image deformation due to the star-trail curvature and differential speed. These effects make the stellar trajectories in the focal plane of the ILMT to be hyperbolic, which are corrected for by the introduction of a TDI optical corrector, designed specifically for the ILMT. Here, we report the first results on the effect of this corrector on the trajectories followed by the stars in the ILMT focal plane. Astrometrically calibrating nine nights of data recorded with the ILMT during its first commissioning phase, we find simple (nearly linear) relations between the CCD-y coordinate and the right ascension (RA) of stars and between the CCD-x coordinate and their declination (DEC), respectively, which confirms that the TDI corrector works very fine in converting the stellar trajectories into straight lines.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".