About this project
From the shape or morphology of a radio source we can infer physical properties of the source and its environment.
To find out what different morphologies are present in the LOFAR radio survey, we use a dimensionality reduction technique known as a Self-Organizing Map.
This is an unsupervised neural network that projects a high-dimensional dataset to a discrete 2-dimensional representation.
The map contains 10 x 10 neurons or prototypes, each represents a cluster of sources.
The radio data we used, with frequencies between 120 and 168Mhz, is part of the LoTSS wide area survey.