AutoSourceID-Light

Fast optical source localization via U-Net and Laplacian of Gaussian

¹ Department of Astrophysics/IMAPP, Radboud University, PO Box 9010, 6500 GL The Netherlands
e-mail: f.stoppa@astro.ru.nl
² Center for Astrophysics and Cosmology, University of Nova Gorica, Vipavska 13, 5000 Nova Gorica, Slovenia
³ High Energy Physics/IMAPP, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands
⁴ Nikhef, Science Park 105, 1098 XG Amsterdam, The Netherlands
⁵ Science Institute, University of Iceland, IS-107 Reykjavik, Iceland
⁶ Instituto de Física Corpuscular, IFIC-UV/CSIC, Valencia, Spain
⁷ Department of Mathematics/IMAPP, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands
⁸ Department of Astronomy, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa
⁹ South African Astronomical Observatory, PO Box 9, Observatory, 7935 Cape Town, South Africa
¹⁰ The Inter-University Institute for Data Intensive Astronomy, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa
¹¹ SRON, Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands
¹² Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium
¹³ Institute for Fundamental Physics of the Universe, Via Beirut 2, 34151 Trieste, Italy

Received: 2 February 2022
Accepted: 11 May 2022

Abstract

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.