Mining Hubble Space Telescope images

We present a unsupervised machine learning technique to explore large sky surveys produced by imaging telescopes. Distinct from previous approaches this technique requires no pre-selection of target galaxy type; instead it automatically identifies objects that are similar. We apply the technique to the five Hubble Space Telescope CANDELS fields and compare the machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. We find that although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, there is a good level of concordance between human and machine classifications. A catalogue and galaxy similarity search is provided for the CANDELS fields at http://www.galaxyml.uk/

Date: 24/03/2017
Time: 16:00
Location: LB252

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olfaction synaptic plasticity robotics machine learning Associative memory Purkinje cell Computer Science Neuromorphic hardware neural networks neuroscience Convolutional Neural Networks PCA Neuronal Morphology optimization Evolutionary algorithms Dendritic computation Persistent homology Deep learning computational modelling Open position cerebellum Neurons computational neuroscience Software development COVID-19 Network connectivity Studentship Homoeostasis Structural plasticity Artificial intelligence