Using Convolutional Neural Networks to identify Gravitational Lenses in Astronomical images. (arXiv:1905.04303v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Davies_A/0/1/0/all/0/1">Andrew Davies</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Serjeant_S/0/1/0/all/0/1">Stephen Serjeant</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bromley_J/0/1/0/all/0/1">Jane M. Bromley</a> (Open University)
The Euclid telescope, due for launch in 2021, will perform an imaging and
slitless spectroscopy survey over half the sky, to map baryon wiggles and weak
lensing. During the survey Euclid is expected to resolve 100,000 strong
gravitational lens systems. This is ideal to find rare lens configurations,
provided they can be identified reliably and on a reasonable timescale. For
this reason we have developed a Convolutional Neural Network (CNN) that can be
used to identify images containing lensing systems. CNNs have already been used
for image and digit classification as well as being used in astronomy for
star-galaxy classification. Here our CNN is trained and tested on Euclid-like
and KiDS-like simulations from the Euclid Strong Lensing Group, successfully
classifying 77% of lenses, with an area under the ROC curve of up to 0.96. Our
CNN also attempts to classify the lenses in COSMOS HST F814W-band images. After
convolution to the Euclid resolution, we find we can recover most systems that
are identifiable by eye. The Python code is available on Github.
The Euclid telescope, due for launch in 2021, will perform an imaging and
slitless spectroscopy survey over half the sky, to map baryon wiggles and weak
lensing. During the survey Euclid is expected to resolve 100,000 strong
gravitational lens systems. This is ideal to find rare lens configurations,
provided they can be identified reliably and on a reasonable timescale. For
this reason we have developed a Convolutional Neural Network (CNN) that can be
used to identify images containing lensing systems. CNNs have already been used
for image and digit classification as well as being used in astronomy for
star-galaxy classification. Here our CNN is trained and tested on Euclid-like
and KiDS-like simulations from the Euclid Strong Lensing Group, successfully
classifying 77% of lenses, with an area under the ROC curve of up to 0.96. Our
CNN also attempts to classify the lenses in COSMOS HST F814W-band images. After
convolution to the Euclid resolution, we find we can recover most systems that
are identifiable by eye. The Python code is available on Github.
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