论文标题
Autosourceid-Light。通过U-NET和LAPLACIAN的快速光源定位
AutoSourceID-Light. Fast Optical Source Localization via U-Net and Laplacian of Gaussian
论文作者
论文摘要
$ \ textbf {aims} $。随着光学宽场望远镜的不断增长的调查速度，以及在仍然年轻，快速和可靠的来源定位时发现瞬变的重要性至关重要。我们提出了AutoSourceId-Light（ASID-L），这是一种创新的框架，它使用计算机视觉技术，可以自然地处理大量数据并在光学图像中快速定位源。 $ \ textbf {methods} $。我们表明，基于U形网络的自动化算法，并通过高斯滤波器的Laplacian（Chen等，1987）增强了算法，从而在来源的定位方面具有出色的性能。一个U-NET（Ronneberger等人，2015年）网络辨别出来自许多不同人工制品的图像中的来源，并将结果传递给高斯滤波器的拉普拉斯式，然后估计确切的位置。 $ \ textbf {results} $。在Meerlicht望远镜的光学图像上的应用显示了该方法的速度和本地化功能。我们将结果与广泛使用的Sextractor（Bertin＆Arnouts 1996）进行了比较，并显示了我们方法的表现。 Autosourceid-Light不仅在低人群和中间的田野中迅速检测到更多的来源，而且尤其是在每平方英尺150多个来源的地区。
$\textbf{Aims}$. 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.
