| Issue |
Emergent Scientist
Volume 2, 2018
|
|
|---|---|---|
| Article Number | 1 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/emsci/2017010 | |
| Published online | 23 February 2018 | |
Research Article
Auto-detection of strong gravitational lenses using convolutional neural networks
The University of Nottingham, University Park,
Nottingham
NG7 2RD, UK
* e-mail: tomrobinson@hotmail.co.uk
Received:
23
July
2017
Accepted:
23
November
2017
We propose a method for the automated detection of strong galaxy-galaxy gravitational lenses in images, utilising a convolutional neural network (CNN) trained on 210 000 simulated galaxy-galaxy lens and non-lens images. The CNN, named LensFinder, was tested on a separate 210 000 simulated image catalogue, with 95% of images classied with at least 98.6% certainty. An accuracy of over 98% was achieved and an area under curve of 0.9975 was determined from the resulting receiver operating characteristic curve. A regional CNN, R-LensFinder, was trained to label lens positions in images, perfectly labelling 80% while partially labelling another 10% correctly.
© J. Pearson et al., Published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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