Abstract
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.
Highlights
All massive objects have gravitational fields that distort spacetime around them, with more massive objects producing stronger distortions
We propose that any image catalogue should consist of images simulated by both Training method 1 (TM1) and Training method 2 (TM2) for a completely trained convolutional neural network (CNN)
We have presented the development of a convolutional neural network, capable of automatically detecting strong galaxy-galaxy lenses from snapshot images
Summary
All massive objects have gravitational fields that distort spacetime around them, with more massive objects producing stronger distortions. Light rays travel along the shortest path (a geodesic), and those passing through this distorted region change direction as they are curved around the object. Such large objects are called gravitational lenses, and include galaxies and galaxy clusters. These are capable of strong, noticeable lensing effects in the form of arcs of light around the lens, which are the result of light rays originating from a light source behind the lens, such as a distant galaxy. If the distortion can be removed, the original appearance of the source galaxy can be obtained along with the mass profile of the lensing galaxy [3]. Gravitational lensing can help constrain the inner mass density profiles of galaxies [4], and when combined with redshift measurements, gravitational lensing features have the potential to aid galaxy evolution models
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