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

Abstract

$\text{Aims}$. 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. $\text{Methods}$. 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. $\text{Results}$. 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.

References in corpus (8)

• The Gaia mission
• Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• The James Webb Space Telescope
• Impact of the SpaceX Starlink Satellites on the Zwicky Transient Facility Survey Observations
• Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge
• MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using Deep Learning
• Deep learning for Sunyaev-Zel'dovich detection in Planck

Cited by in corpus (4)

• Automated Detection of Satellite Trails in Ground-Based Observations Using U-Net and Hough Transform
• AutoSourceID-Classifier. Star-Galaxy Classification using a Convolutional Neural Network with Spatial Information
• The Evryscope Fast Transient Engine: Real-Time Detection for Rapidly Evolving Transients
• AutoSourceID-FeatureExtractor. Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation