Finding strong lenses in CFHTLS using convolutional neural networks

@article{Jacobs2017FindingSL,
  title={Finding strong lenses in CFHTLS using convolutional neural networks},
  author={Colin Jacobs and Karl Glazebrook and Thomas E. Collett and Anupreeta More and Christopher Mccarthy},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2017},
  volume={471},
  pages={167-181}
}
We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An ensemble of four convolutional neural networks was trained on images of simulated galaxy-galaxy lenses. The training sets consisted of a total of 62,406 simulated lenses and 64,673 non-lens negative examples generated with two different… Expand
Identifying strong lenses with unsupervised machine learning using convolutional autoencoder
In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian GaussianExpand
The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples
We explore the effectiveness of deep learning convolutional neural networks (CNNs) for estimating strong gravitational lens mass model parameters. We have investigated a number of practicalitiesExpand
Deep convolutional neural networks as strong gravitational lens detectors
Future large-scale surveys with high resolution imaging will provide us with a few $10^5$ new strong galaxy-scale lenses. These strong lensing systems however will be contained in large data amountsExpand
LensFlow: A Convolutional Neural Network in Search of Strong Gravitational Lenses
In this work, we present our classification algorithm to identify strong gravitational lenses from wide-area surveys using machine learning convolutional neural network; LensExtractor. We train andExpand
Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS
Convolutional Neural Networks (ConvNets) are one of the most promising methods for identifying strong gravitational lens candidates in survey data. We present two ConvNet lens-finders that we haveExpand
Auto-detection of strong gravitational lenses using convolutional neural networks
TLDR
A convolutional neural network trained to label lens positions in images, perfectly labelling 80% while partially labelling another 10% correctly, achieving accuracy of over 98% and an area under curve of 0.9975 was determined from the resulting receiver operating characteristic curve. Expand
A comparative study of convolutional neural networks for the detection of strong gravitational lensing
As we enter the era of large-scale imaging surveys with the up-coming telescopes such as LSST and SKA, it is envisaged that the number of known strong gravitational lensing systems will increaseExpand
Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique
TLDR
A strong lensing identification approach that utilizes a feature extraction method from computer vision, the Histogram of Oriented Gradients (HOG), to capture edge patterns of arcs, indicating that HOG captures much of the morphological complexity of the arc finding problem. Expand
An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks
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 aExpand
Radio Galaxy Zoo: compact and extended radio source classification with deep learning
Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neuralExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 71 REFERENCES
Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks
The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyse sources. Indeed, this is the case for the search for strongExpand
CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding
Galaxy-scale strong gravitational lensing is not only a valuable probe of the dark matter distribution of massive galaxies, but can also provide valuable cosmological constraints, either by studyingExpand
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
TLDR
The ability to better recover detailed features from low-signal-to-noise and low angular resolution imaging data significantly increases the ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope and the Hubble and James Webb space telescopes. Expand
Space Warps – I. Crowdsourcing the discovery of gravitational lenses
We describe SpaceWarps, a novel gravitational lens discovery service that yields samples of high purity and completeness through crowd-sourced visual inspection. Carefully produced colour compositeExpand
A neural network gravitational arc finder based on the Mediatrix filamentation method
Context. Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc findersExpand
Rotation-invariant convolutional neural networks for galaxy morphology prediction
TLDR
A deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry is developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project. Expand
The CFHTLS strong lensing legacy survey - I. Survey overview and T0002 release sample
AIMS: We present data from the CFHTLS Strong Lensing Legacy Survey (SL2S). Due to the unsurpassed combined depth, area and image quality of the Canada-France-Hawaii Legacy Survey it is becomingExpand
Automated detection of galaxy-scale gravitational lenses in high resolution imaging data
We expect direct lens modeling to be the key to successful and meaningful automated strong galaxy-scale gravitational lens detection. We have implemented a lens-modeling robot that treats everyExpand
CHITAH: Strong-gravitational-lens hunter in imaging surveys
Strong gravitationally lensed quasars provide powerful means to study galaxy evolution and cosmology. Current and upcoming imaging surveys will contain thousands of new lensed quasars, augmenting theExpand
A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
We present a catalog of visual like H-band morphologies of $\sim50.000$ galaxies ($H_{f160w} \sim1.25$. The algorithm is trained on GOODS-S for which visual classifications are publicly available andExpand
...
1
2
3
4
5
...