Deep Learning of Astronomical Features with Big Data

被引:0
|
作者
Lieu, Maggie [1 ]
Baines, Deborah [1 ]
Giordano, Fabrizio [1 ]
Merin, Bruno [1 ]
Arviset, Christophe [1 ]
Altieri, Bruno [1 ]
Conversi, Luca [1 ]
Carry, Benoit [2 ]
机构
[1] ESA, ESAC, Madrid, Spain
[2] Univ Cote Azur, Observ Cote Azur, CNRS, Lab Lagrange, Nice, France
来源
ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXVIII | 2019年 / 523卷
关键词
GALAXY ZOO;
D O I
暂无
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
In Astronomy, there is a tendency to build machine learning codes for very specific object detection in images. The classification of asteroids and non-asteroids should be no different than the classification of asteroids, stars, galaxies, cosmic rays, ghosts or any other artefact found in astronomical data. In computer science, it is not uncommon for machine learning to train on hundreds of thousands of object categories, so why are we not there yet? I will talk about image classification with deep learning and how we can make use of existing tools such as the ESA science archive, ESAsky and citizen science to help realise the full potential of object detection and image classification in Astronomy.
引用
收藏
页码:49 / 58
页数:10
相关论文
共 50 条
  • [1] Machine Learning for Astronomical Big Data Processing
    Xu, Long
    Yan, Yihua
    2017 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), 2017,
  • [2] Big Data and Deep Learning
    Wilamowski, B. M.
    Wu, Bo
    Korniak, Janusz
    INES 2016 20TH JUBILEE IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT ENGINEERING SYSTEMS, 2016, : 11 - 16
  • [3] Deep Learning for Big Data
    Correia, Filipe
    Madureira, Ana
    Bernardino, Jorge
    INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021, 2022, 419 : 205 - 215
  • [4] A Survey on Deep Learning in Big Data
    Gheisari, Mehdi
    Wang, Guojun
    Bhuiyan, Md Zakirul Alam
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 2, 2017, : 173 - 180
  • [5] Deep Learning for Hydrophone Big Data
    McQuay, Colter
    Sattar, Farook
    Driessen, Peter F.
    2017 IEEE PACIFIC RIM CONFERENCE ON COMMUNICATIONS, COMPUTERS AND SIGNAL PROCESSING (PACRIM), 2017,
  • [6] Deep Learning for Big Data Analytics
    Bathla, Gourav
    Aggarwal, Himanshu
    Rani, Rinkle
    ADVANCES IN COMPUTING AND INTELLIGENT SYSTEMS, ICACM 2019, 2020, : 391 - 399
  • [7] A survey on deep learning for big data
    Zhang, Qingchen
    Yang, Laurence T.
    Chen, Zhikui
    Li, Peng
    INFORMATION FUSION, 2018, 42 : 146 - 157
  • [8] Big Data: Astronomical or Genomical?
    Stephens, Zachary D.
    Lee, Skylar Y.
    Faghri, Faraz
    Campbell, Roy H.
    Zhai, Chengxiang
    Efron, Miles J.
    Iyer, Ravishankar
    Schatz, Michael C.
    Sinha, Saurabh
    Robinson, Gene E.
    PLOS BIOLOGY, 2015, 13 (07)
  • [9] ASTRONOMICAL SURVEYS AND BIG DATA
    Mickaelian, Areg M.
    BALTIC ASTRONOMY, 2016, 25 (01) : 75 - 88
  • [10] Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
    Thuong-Cang Phan
    Anh-Cang Phan
    Hung-Phi Cao
    Thanh-Ngoan Trieu
    APPLIED SCIENCES-BASEL, 2022, 12 (13):