Galaxy Spectra Neural Networks(GaSNets). I. Searching for Strong Lens Candidates in eBOSS Spectra Using Deep Learning  

作　　者：Fucheng Zhong Rui Li Nicola R.Napolitano 

机构地区：[1]School of Physics and Astronomy,Sun Yat-sen University,Zhuhai Campus,Zhuhai 519082,China [2]CSST Science Center for Guangdong-Hong Kong-Macao Great Bay Area,Zhuhai 519082,China [3]School of Astronomy and Space Science,University of Chinese Academy of Sciences,Beijing 100049,China [4]National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China

出　　处：《Research in Astronomy and Astrophysics》2022年第6期142-169,共28页天文和天体物理学研究（英文版）

基　　金：the science research grants from the China Manned Space Project(CMSCSST-2021-A01);the support from K.C.Wong Education Foundation;financial support from the“One hundred top talent program of Sun Yatsen University”grant No.71000-18841229;from the Research Fund for International Scholars of the National Science Foundation of China,grant No.12150710511。

摘　　要：With the advent of new spectroscopic surveys from ground and space,observing up to hundreds of millions of galaxies,spectra classification will become overwhelming for standard analysis techniques.To prepare for this challenge,we introduce a family of deep learning tools to classify features in one-dimensional spectra.As the first application of these Galaxy Spectra neural Networks(Ga SNets),we focus on tools specialized in identifying emission lines from strongly lensed star-forming galaxies in the e BOSS spectra.We first discuss the training and testing of these networks and define a threshold probability,PL,of 95%for the high-quality event detection.Then,using a previous set of spectroscopically selected strong lenses from e BOSS,confirmed with the Hubble Space Telescope(HST),we estimate a completeness of~80%as the fraction of lenses recovered above the adopted PL.We finally apply the Ga SNets to~1.3M eBOSS spectra to collect the first list of~430 new high-quality candidates identified with deep learning from spectroscopy and visually graded as highly probable real events.A preliminary check against ground-based observations tentatively shows that this sample has a confirmation rate of 38%,in line with previous samples selected with standard(no deep learning)classification tools and confirmed by the HST.This first test shows that machine learning can be efficiently extended to feature recognition in the wavelength space,which will be crucial for future surveys like 4MOST,DESI,Euclid,and the China Space Station Telescope.

关 键 词：gravitational lensing strong-surveys-techniques SPECTROSCOPIC 

分 类 号：P111[天文地球—天文学] P15