论文标题
使用多个神经网络对紧凑星系，恒星和类星体的光度鉴定
Photometric identification of compact galaxies, stars and quasars using multiple neural networks
论文作者
论文摘要
我们提出了Margnet，这是一种基于深度学习的分类器，用于使用Sloan Digital Sky Survey（SDSS）数据版本16（DR16）目录中的光度参数和图像来识别恒星，类星体和紧凑的星系。 Margnet包括卷积神经网络（CNN）和人工神经网络（ANN）体系结构的组合。使用经过精心策划的数据集，该数据集由240,000个紧凑型物体和另外150,000个微弱对象组成，直接从数据中学习分类，从而最大程度地减少了对人类干预的需求。 MARGNET是第一个专门关注紧凑星系的分类器，并且比其他方法更好，可以从恒星和类星体中分类紧凑的星系，即使是在较弱的大小上也是如此。在这种深度学习体系结构中，这种模型和功能工程将在识别正在进行的和即将进行的调查中的对象（例如Dark Energy Survey（DES）和Vera C. Rubin天文台的图像）方面提供更大的成功。
We present MargNet, a deep learning-based classifier for identifying stars, quasars and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) catalogue. MargNet consists of a combination of Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) architectures. Using a carefully curated dataset consisting of 240,000 compact objects and an additional 150,000 faint objects, the machine learns classification directly from the data, minimising the need for human intervention. MargNet is the first classifier focusing exclusively on compact galaxies and performs better than other methods to classify compact galaxies from stars and quasars, even at fainter magnitudes. This model and feature engineering in such deep learning architectures will provide greater success in identifying objects in the ongoing and upcoming surveys, such as Dark Energy Survey (DES) and images from the Vera C. Rubin Observatory.
