<tt> <scp>PyLMT</scp> </tt>: a transient detection pipeline for the 4-m International Liquid Mirror Telescope
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT The International Liquid Mirror Telescope (ILMT) is a 4-m aperture, zenith-pointing telescope with a field of view of 22$^{\prime }$, situated in the foothills of the Himalayas. The telescope operates in continuous survey mode, making it a useful instrument for time-domain astronomy, particularly for detecting transients, variable stars, active galactic nuclei variability, and asteroids. This paper presents thePyLMT transient detection pipeline to detect such transient/varying sources in the ILMT images. The pipeline utilizes the image subtraction technique to compare a pair of images from the same field, identifying such sources in subtracted images with the help of convolutional neural network (CNN)-based real/bogus classifiers. The test accuracies determined for the real/bogus classifiers ranged from 94 per cent to 98 per cent. The resulting precision of the pipeline calculated over candidate alerts in the ILMT frames is 0.91. It also houses a CNN-aided transient candidate classifier that classifies the transient/variable candidates based on host morphology. The test accuracy of the candidate classifier is 98.6 per cent. It has the provision to identify catalogued asteroids and other Solar system objects using public data bases. The median execution time of the pipeline is approximately 29 min per image of 17 min exposure. Relevant CNNs have been trained on data acquired with the ILMT during the cycle of 2022 October–November. Subsequent tests on those images have confirmed the detection of numerous catalogued asteroids, variable stars, and other uncatalogued sources. The pipeline has been operational and has detected 12 extragalactic transients, including 2 new discoveries in the 2023 November–2024 May observation cycle.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it