Developing data solutions for observatories

We create applications to access diverse research data sets, across multiple disciplines, and develop comprehensive data processing platforms for the complex instrument suites of major astronomical observatories.

Our group also includes researchers in computing and statistics, including:

  • expertise in machine learning
  • natural language processing
  • Bayesian modeling methods
  • analysis of multi-layered and structured data, with applications ranging from astrophysics to bioinfomatics and computational ecology.

Learn more about the projects we are undertaking, the researchers engaged in them, and who you can contact to get involved.

Galaxy classification by deep learning

The complex and varied appearances of galaxies hold clues to their formation pathway, yet our interpretation of their ‘morphological class’ is often driven by the specific type and quality of data we use.

This poses problems for reproducibility and limits the applicability of those classes when considering differing data types.

This project will use machine learning and large astronomical data sets to build observation-agnostic classification tools, working towards generating a foundation model of galaxies that can be used in various applications, including observation and instrument design, and data-model comparisons.

Supervisor: Richard McDermid