TY  - JOUR
AU  - Stoppa, F.
AU  - Vreeswijk, P.
AU  - Bloemen, S.
AU  - Bhattacharyya, S.
AU  - Caron, S.
AU  - Johannesson, G.
AU  - Ruiz de Austri, R.
AU  - van den Oetelaar, C.
AU  - Zaharijas, G.
AU  - Groot, P. J.
AU  - Cator, E.
AU  - Nelemans, G.
PY  - 2022
DA  - 2022//
TI  - AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian
T2  - Astron. Astrophys.
JO  - Astronomy & Astrophysics
SP  - A109 - 8pp
VL  - 662
PB  - Edp Sciences S A
KW  - astronomical databases
KW  - miscellaneous
KW  - methods
KW  - data analysis
KW  - stars
KW  - imaging
KW  - techniques
KW  - image processing
AB  - Aims. With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods. We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results. Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method. ASID-L rapidly detects more sources not only in low- and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available.
SN  - 0004-6361
UR  - https://arxiv.org/abs/2202.00489
UR  - https://doi.org/10.1051/0004-6361/202243250
DO  - 10.1051/0004-6361/202243250
LA  - English
N1  - WOS:000818665600009
ID  - Stoppa_etal2022
ER  -