An Automated Photometric Pipeline for the ILMT data
Bibliographic record
Abstract
The International Liquid Mirror Telescope (ILMT) is a 4-meter survey telescope continuously observing towards the zenith in the SDSS g’, r’, and i’ bands. This survey telescope is designed to detect various astrophysical transients (for example, supernovae) and very faint objects like multiply-imaged quasars and low surface brightness galaxies. A single scan of a 22′ strip of sky contains a large amount of photometric information. To process this type of data, it becomes critical to have tools or pipelines that can handle it efficiently and accurately with minimal human biases. We offer a fully automated pipeline generated in Python to perform aperture photometry over the ILMT data acquired with the CCD in Time Delayed Integration (TDI) mode. The instrumental magnitudes are calibrated with respect to the Pan-STARRS-1 catalogue. The light curves generated from the calibrated magnitudes will allows us to characterize the objects as variable stars or rapidly decaying transients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.054 |
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".