Serendipitous detection of orbital debris by the International Liquid Mirror Telescope
Bibliographic record
Abstract
The International Liquid Mirror Telescope is a 4-m zenith-pointing optical telescope that employs a rotating liquid primary mirror. Located in the Indian Himalayas, it began operations in October 2022. The telescope is equipped with a CCD camera that has a 22 x 22 arcmin field of view and employs time-delay integration readout to compensate for the Earth’s rotation. While its primary purpose is to conduct astronomical survey observations using broad-band filters, the telescope is also sensitive to objects in Earth orbit that pass through its field of view, leaving detectable streaks. We have examined all images obtained during the first year of observations and determined the transit times and position angles of all detected objects. These were compared with publicly available two-line elements, propagated to the time of observation, in order to identify cataloged objects. A total of 301 streaks were found in 1838 images. Of these, 64% were identified with cataloged objects. Most of the identified objects are in low-Earth orbit, in the altitude range of 400–1600 km. The apparent magnitudes of the identified objects range from 3.6 to 15.1 in the V band. It was also possible to infer angular rates, apparent magnitudes and altitudes for 29% of the unidentified objects. The V-band magnitudes range from 6.4 to 19.5 and the estimated altitudes range from 285 to over 300,000 km.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 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".