In the era of sky surveys like Palomar Transient Factory (PTF), Zwicky Transient Facility (ZTF) and the upcoming Vera Rubin Observatory (VRO) and ILMT, a plethora of image data will be available. ZTF scans the sky with a field of view of 48 deg2 and VRO will have a FoV of 9.6 deg2 but with a much larger aperture. The 4m ILMT covers a 22′ wide strip of the sky. Being a zenith telescope, ILMT has several advantages like low observation air mass, best image quality, minimum light pollution and no pointing time loss. Transient detection requires all these imaging data to be processed through a Difference Imaging Algorithm (DIA) followed by subsequent identification and classification of transients. The ILMT is also expected to discover several known and unknown astrophysical objects including transients. Here, we propose a pipeline with an image subtraction algorithm and a convolutional neural network (CNN) based automated transient discovery and classification system. The pipeline was tested on ILMT data and the transients as well as variable candidates were recovered and classified.
@article{arxiv.2311.04716,
title = {Automated transient detection in the context of the 4m ILMT},
author = {Kumar Pranshu and Bhavya Ailawadhi and Talat Akhunov and Ermanno Borra and Monalisa Dubey and Naveen Dukiya and Jiuyang Fu and Baldeep Grewal and Paul Hickson and Brajesh Kumar and Kuntal Misra and Vibhore Negi and Ethen Sun and Jean Surdej},
journal= {arXiv preprint arXiv:2311.04716},
year = {2023}
}
Comments
9 pages, 3 figures, accepted for publication in the Bulletin of Li\`ege Royal Society of Sciences as a part of 3rd Belgo-Indian Network for Astronomy and Astrophysics (BINA) workshop, 22-24 March 2023