We analyse VLBI and optical images of AGNs and their host galaxies and look for statistical correlations between the shape and orientation of the galaxy and the direction of the jet. We utilise the Astrogeo catalogue, which has over 9000 VLBI sources, many of those with a clear core-jet like structure that allows for the jet position angle to be reliably determined. We then use the VLBI source positions to search for optical counterparts within various optical surveys. In order to parameterise the orientation and shape of the host galaxy, we fitted a Gaussian elliptical model to the optical image, taking the PSF into account. We check our own shape parameters from this fit against the ones provided by the optical surveys. As of yet, no clear correlation between the galaxy morphology and the jet direction is seen.

Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.