An Extended Catalog of Galaxy-Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks

Jacobs C.; Collett T.; Glazebrook K.; Buckley-Geer E.; Diehl H. T.; Lin H.; McCarthy C.; Qin A. K.; Odden C.; Escudero M. Caso; Dial P.; Yung V. J.; Gaitsch S.; Pellico A.; Lindgren K. A.; Abbott T. M. C.; Annis J.; Avila S.; Brooks D.; Burke D. L.; Carnero Rosell A.; Kind M. Carrasco; Carretero J.; da Costa L. N.; De Vicente J.; Fosalba P.; Frieman J.; Garcia-Bellido J.; Gaztanaga E.; Goldstein D. A.; Gruen D.; Gruendl R. A.; Gschwend J.; Hollowood D. L.; Honscheid K.; Hoyle B.; James D. J.; Krause E.; Kuropatkin N.; Lahav O.; Lima M.; Maia M. A. G.; Marshall J. L.; Miquel R.; Plazas A. A.; Roodman A.; Sanchez E.; Scarpine V.; Serrano S.; Sevilla-Noarbe I.; Smith M.; Sobreira F.; Suchyta E.; Swanson M. E. C.; Tarle G.; Vikram V.; Walker A. R.; Zhang Y.

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An Extended Catalog of Galaxy-Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks

机译：使用卷积神经网络在DES中发现的Galaxy-Galaxy强引力镜片的扩展目录

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We search Dark Energy Survey (DES) Year 3 imaging for galaxy-galaxy strong gravitational lenses using convolutional neural networks, extending previous work with new training sets and covering a wider range of redshifts and colors. We train two neural networks using images of simulated lenses, then use them to score postage-stamp images of 7.9 million sources from DES chosen to have plausible lens colors based on simulations. We examine 1175 of the highest-scored candidates and identify 152 probable or definite lenses. Examining an additional 20,000 images with lower scores, we identify a further 247 probable or definite candidates. After including 86 candidates discovered in earlier searches using neural networks and 26 candidates discovered through visual inspection of blue-near-red objects in the DES catalog, we present a catalog of 511 lens candidates.

机译：搜索暗能调查（DES）第3年3年度使用卷积神经网络对Galaxy-Galaxy强大的引力镜头进行了成像，以延长了以前的培训集，覆盖了更广泛的红移和颜色。我们使用模拟镜头的图像训练两个神经网络，然后使用它们以根据仿真选择790万来源的邮票图像790万来源。我们研究了1175年的最高评分候选人，并确定了152个可能或明确的镜头。检查具有较低分数的额外20,000个图像，我们识别另一个可能的247个可能的或明确的候选人。在包括早期搜索中发现的86名候选人，使用神经网络和26个通过视觉检查DES目录中的蓝近红对象发现的候选人，我们呈现了511个镜头候选的目录。

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《The Astrophysical Journal. Supplement Series》 |2019年第1期|共11页
作者
Jacobs C.; Collett T.; Glazebrook K.; Buckley-Geer E.; Diehl H. T.; Lin H.; McCarthy C.; Qin A. K.; Odden C.; Escudero M. Caso; Dial P.; Yung V. J.; Gaitsch S.; Pellico A.; Lindgren K. A.; Abbott T. M. C.; Annis J.; Avila S.; Brooks D.; Burke D. L.; Carnero Rosell A.; Kind M. Carrasco; Carretero J.; da Costa L. N.; De Vicente J.; Fosalba P.; Frieman J.; Garcia-Bellido J.; Gaztanaga E.; Goldstein D. A.; Gruen D.; Gruendl R. A.; Gschwend J.; Hollowood D. L.; Honscheid K.; Hoyle B.; James D. J.; Krause E.; Kuropatkin N.; Lahav O.; Lima M.; Maia M. A. G.; Marshall J. L.; Miquel R.; Plazas A. A.; Roodman A.; Sanchez E.; Scarpine V.; Serrano S.; Sevilla-Noarbe I.; Smith M.; Sobreira F.; Suchyta E.; Swanson M. E. C.; Tarle G.; Vikram V.; Walker A. R.; Zhang Y.;
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作者单位

Swinburne Univ Technol Ctr Astrophys &

Supercomp POB 218 Hawthorn Vic 3122 Australia;

Univ Portsmouth Inst Cosmol &

Gravitat Portsmouth PO1 3FX Hants England;

Swinburne Univ Technol Ctr Astrophys &

Supercomp POB 218 Hawthorn Vic 3122 Australia;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

Swinburne Univ Technol Sch Software &

Elect Engn POB 218 Hawthorn Vic 3122 Australia;

Swinburne Univ Technol Sch Software &

Elect Engn POB 218 Hawthorn Vic 3122 Australia;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

Natl Opt Astron Observ Cerro Tololo Interamer Observ Casilla 603 La Serena Chile;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

UAM CSIC Inst Fis Teor E-28049 Madrid Spain;

UCL Dept Phys &

Astron Gower St London WC1E 6BT England;

Stanford Univ Kavli Inst Particle Astrophys &

Cosmol POB 2450 Stanford CA 94305 USA;

Ctr Invest Energet Medioambientales &

Tecnol CIEM Madrid Spain;

Univ Illinois Dept Astron 1002 W Green St Urbana IL 61801 USA;

Barcelona Inst Sci &

Technol IFAE Campus UAB E-08193 Bellaterra Barcelona Spain;

Lab Interinst eAstron LIneA Rua Gal Jose Cristino 77 BR-20921400 Rio De Janeiro RJ Brazil;

Ctr Invest Energet Medioambientales &

Tecnol CIEM Madrid Spain;

IEEC E-08034 Barcelona Spain;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

UAM CSIC Inst Fis Teor E-28049 Madrid Spain;

IEEC E-08034 Barcelona Spain;

CALTECH 1200 East Calif Blvd MC 249-17 Pasadena CA 91125 USA;

Stanford Univ Kavli Inst Particle Astrophys &

Cosmol POB 2450 Stanford CA 94305 USA;

Natl Ctr Supercomp Applicat 1205 West Clark St Urbana IL 61801 USA;

Lab Interinst eAstron LIneA Rua Gal Jose Cristino 77 BR-20921400 Rio De Janeiro RJ Brazil;

Santa Cruz Inst Particle Phys Santa Cruz CA 95064 USA;

Ohio State Univ Ctr Cosmol &

Astroparticle Phys Columbus OH 43210 USA;

Max Planck Inst Extraterr Phys Giessenbachstr D-85748 Garching Germany;

Harvard Smithsonian Ctr Astrophys Cambridge MA 02138 USA;

Univ Arizona Steward Observ Dept Astron 933 North Cherry Ave Tucson AZ 85721 USA;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

UCL Dept Phys &

Astron Gower St London WC1E 6BT England;

Lab Interinst eAstron LIneA Rua Gal Jose Cristino 77 BR-20921400 Rio De Janeiro RJ Brazil;

Lab Interinst eAstron LIneA Rua Gal Jose Cristino 77 BR-20921400 Rio De Janeiro RJ Brazil;

Texas A&

M Univ George P &

Cynthia Woods Mitchell Inst Fundamenta College Stn TX 77843 USA;

Barcelona Inst Sci &

Technol IFAE Campus UAB E-08193 Bellaterra Barcelona Spain;

Princeton Univ Dept Astrophys Sci Peyton Hall Princeton NJ 08544 USA;

Stanford Univ Kavli Inst Particle Astrophys &

Cosmol POB 2450 Stanford CA 94305 USA;

Ctr Invest Energet Medioambientales &

Tecnol CIEM Madrid Spain;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

IEEC E-08034 Barcelona Spain;

Ctr Invest Energet Medioambientales &

Tecnol CIEM Madrid Spain;

Univ Southampton Sch Phys &

Astron Southampton SO17 1BJ Hants England;

Lab Interinst eAstron LIneA Rua Gal Jose Cristino 77 BR-20921400 Rio De Janeiro RJ Brazil;

Oak Ridge Natl Lab Comp Sci &

Math Div Oak Ridge TN 37831 USA;

Natl Ctr Supercomp Applicat 1205 West Clark St Urbana IL 61801 USA;

Univ Michigan Dept Phys Ann Arbor MI 48109 USA;

Argonne Natl Lab 9700 South Cass Ave Lemont IL 60439 USA;

Natl Opt Astron Observ Cerro Tololo Interamer Observ Casilla 603 La Serena Chile;

Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA;

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原文格式 PDF
正文语种 eng
中图分类天体物理学;天文学;
关键词
gravitational lensing: strong; methods: data analysis; methods: statistical; surveys;

机译：引力透镜：强;方法：数据分析;方法：统计;调查;

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专利

1. An Extended Catalog of Galaxy-Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks [J] . Jacobs C., Collett T., Glazebrook K., The Astrophysical Journal. Supplement Series . 2019,第1期

机译：使用卷积神经网络在DES中发现的Galaxy-Galaxy强引力镜片的扩展目录
2. Fast automated analysis of strong gravitational lenses with convolutional neural networks [J] . Hezaveh Yashar D., Levasseur Laurence Perreault, Marshall Philip J. Nature . 2017,第7669期

机译：利用卷积神经网络快速自动分析强重力透镜
3. Deep convolutional neural networks as strong gravitational lens detectors [J] . C. Schaefer, M. Geiger, T. Kuntzer, Astronomy and astrophysics . 2018,第8期

机译：深卷积神经网络作为强大的重力透镜检测器
4. Improving Gravitational Wave Detection with 2D Convolutional Neural Networks [C] . Siyu Fan, Yisen Wang, Yuan Luo, International Conference on Pattern Recognition . 2021

机译：用2D卷积神经网络改善引力波检测
5. Environments of strong gravitational lenses. [D] . Momcheva, Ivelina Gospodinova. 2009

机译：强重力透镜的环境。
6. Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement [O] . Philip Teare, Michael Fishman, Oshra Benzaquen, 2017

机译：使用双深层卷积神经网络和遗传发现的假色输入增强对乳房X线照片进行恶性检测
7. An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks [O] . C. Jacobs, T. Collett, K. Glazebrook, 2019

机译：在使用卷积神经网络中发现的Galaxy-Galaxy强大引力镜片的扩展目录

An Extended Catalog of Galaxy-Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks

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