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
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