PyLMT: a transient detection pipeline for the 4-m International Liquid Mirror Telescope
Feb 1, 202521 pages
Published in:
- Mon.Not.Roy.Astron.Soc. 538 (2025) 1, 133-152
- Published: Feb 7, 2025
e-Print:
- 2502.00556 [astro-ph.IM]
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Abstract: (Oxford University Press)
The International Liquid Mirror Telescope (ILMT) is a 4-m aperture, zenith-pointing telescope with a field of view of 22′, 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.Note:
- 21 pages, 26 figures, accepted for publication in MNRAS
- telescopes
- surveys
- software: machine learning
- minor planets, asteroids: general
- stars: variables: general
- transients: supernovae
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