The full Square Kilometer Array is expected to increase the population of currently known pulsars (~2,900) by more than 10-fold, a large fraction of which should already be found during Phase I. It is therefore timely to research and develop automated methods for knowledge extraction that require little to no human intervention. The integrated pulse profile of a radio pulsar—composed of one or more components distributed over the rotational phase—represents one of its defining characteristics. The pulsar profile at a given observing frequency is generally stable over decades, and often evolves with frequency: a key to study the structure of its radio emitting magnetosphere. Historically, efforts to classify and interpret pulsar profile morphology were performed to a large extent by eye, with recent examples guided by supervised methods. Conversely, unsupervised methods have the potential to unravel faint features and possibly unknown scaling and morphological relations between different pulsars. In this talk, I explore a set of pulsar profiles by applying principles of graph theory. After introducing context and method, I will discuss analysis results and their potential physical implications. I will also introduce advanced visualisations specifically designed to investigate the graph and its components.

Invited presentation, European Astronomical Society Annual Meeting (formerly known as EWASS), Special session 32b: Machine learning and visualisation in data intensive era.