Study level

  • PhD
  • Master of Philosophy


Topic status

We're looking for students to study this topic.

Research centre


Dr Michael Cowley
Division / Faculty
Faculty of Science

External supervisors

  • Professor Andrew Hopkins, Australian Astronomical Optics


The cosmic star formation history (SFH) describes how the rate of star formation in the universe has evolved. This has been measured with increasing precision in recent decades. Using highly sensitive radio detections, this cosmic star formation history can be determined to the highest level of precision yet.

The Evolutionary Map of the Universe (EMU) radio survey will measure up to 30 million radio sources in the coming few years. We estimate half of these will arise from star formations.

Using the EMU survey data, we can distinguish radio emissions from star formations in comparison to emissions from supermassive black holes. This will be the largest sample of star-forming galaxies ever used to probe the cosmic star formation history.

Research activities

As part of this research project, you will:

  • measure samples of radio sources from the EMU survey data
  • identify star-forming galaxy samples using ancillary multiwavelength data
  • measure star formation rates (SFR) in galaxies using a calibration from radio luminosities
  • construct luminosity functions and distribution functions of star formation rates for galaxy samples
  • calculate the cosmic star formation history as a function of redshift for the differently partitioned samples.

The galaxy samples will be partitioned by mass, environment, metallicity and redshift.


Upon conclusion of this research, you'll produce the most robust measurement yet of the cosmic star formation history probing the latter half of the universe's history.

This research can lead to high impact publications.

You'll also have opportunities to netwrok with astronomers based in various institutions around the world.

Skills and experience

The analysis and interpretation of the data will involve custom-designed data processing. Therefore, familiarity with a programming language, such as Python or IDL, is desirable.


You may be eligible to apply for a research scholarship.

Explore our research scholarships


Contact the supervisor for more information.