Present status of the 4-m ILMT data reduction pipeline: application to space debris detection and characterization
Bibliographic record
Abstract
The 4-m International Liquid Mirror Telescope (ILMT) located at the ARIES Observatory (Devasthal, India) has been designed to scan at a latitude of +29° 22’ 26” a band of sky having a width of about half a degree in the Time Delayed Integration (TDI) mode. Therefore, a special data-reduction and analysis pipeline to process online the large amount of optical data being produced has been dedicated to it. This requirement has led to the development of the 4-m ILMT data reduction pipeline, a new software package built with Python in order to simplify a large number of tasks aimed at the reduction of the acquired TDI images. This software provides astronomers with specially designed data reduction functions, astrometry and photometry calibration tools. In this paper we discuss the various reduction and calibration steps followed to reduce TDI images obtained in May 2015 with the Devasthal 1.3m telescope. We report here the detection and characterization of nine space debris present in the TDI frames.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".