AutoSourceID-Light. Fast Optical Source Localization via U-Net and Laplacian of Gaussian

TL;DR

AutoSourceID-Light (ASID-L) is a fast, reliable source localization framework using U-Net and Laplacian of Gaussian filters, outperforming traditional methods in crowded optical images for transient detection.

Contribution

We introduce AutoSourceID-Light, a novel deep learning-based framework combining U-Net and Laplacian of Gaussian filtering for rapid and accurate source localization in optical images.

Findings

ASID-L outperforms SExtractor in speed and accuracy.

Effective in highly crowded fields with over 150 sources per square arcminute.

Demonstrated on MeerLICHT telescope data with superior results.

Abstract

$\textbf{Aims}$. 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. $\textbf{Methods}$. We show that the AutoSourceID-Light algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter (Chen et al. 1987) enables outstanding performances in the localization of sources. A U-Net (Ronneberger et al. 2015) 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. $\textbf{Results}$. Application on optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with the widely used SExtractor (Bertin & Arnouts 1996) and show the out-performances of our method. AutoSourceID-Light rapidly detects more sources not only in low and mid crowded fields, but particularly in areas with more than 150 sources per square arcminute.