Detection and Identification of Asteroids with the 4-m ILMT
Bibliographic record
Abstract
A very unique strength of the Devasthal Observatory is its capability of detecting optical transients with the 4-m International Liquid Mirror Telescope (ILMT) and to rapidly follow them up using the 1.3-m Devasthal Fast Optical Telescope (DFOT) and/or the 3.6-m Devasthal Optical Telescope (DOT), installed right next to it. In this context, we have inspected 20 fields observed during nine consecutive nights in October-November 2022 during the first commissioning phase of the ILMT. Each of these fields has an angular extent of 22′ in declination by 9×22′ in right ascension. Combining both a visual search for optical transients and an automatic search for these using an image subtraction technique (see Pranshu et al., 2024), we report a total of 232 significant transient candidates. After consulting the Minor Planet Center database of asteroids, we could identify among these 219 positions of known asteroids brighter than V = 22. These correspond to the confirmed positions of 78 distinct known asteroids. Analysis of the remaining CCD frames covering 19 more fields (out of 20) should lead to an impressive number of asteroids observed in only nine nights. The conclusion is that in order to detect and characterize new supernovae, micro-lensing events, highly variable stars, multiply imaged quasars, etc. among the ILMT optical transients, we shall first have to identify all known and new asteroids. Thanks to its large diameter and short focal length (f/D ≃ 2.4), the ILMT turns out to be an excellent asteroid hunter.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".