Monday

Gravitational lenses are useful for putting constraints on values of the Hubble constant, w the equation of state, dark matter substructure, and for studying high-redshift galaxies at high spatial and flux magnification. Rare lens systems, for example the Jackpot lens, are particularly useful for w and halo profiles. However rare lens systems can only be found with a large lens samples. The Euclid telescope, due for launch in 2020, 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. For this reason I have developed a new method for finding gravitational lenses using Convolutional Neural Networks (CNNs). CNNs have already been used for image and digit classification as well as being used in astronomy for star-galaxy classification. Using simulated lenses from the EUCLID group I have developed a CNN capable of classifying up to 80 percent of these images correctly.