Dr Julie Banfield

ANU Research School of Astronomy and Astrophysics

ANU College of Physical and Mathematical Sciences

Abstract:  Super-massive black holes are a fundamental building block of galaxies like our own Milky Way. However, astronomers and physicists know little about how they form, grow to be a billion times the mass of our own Sun, and affect the formation of galaxies, stars, planets, and life.  With the next generation radio telescopes coming on line, we are currently living in a “golden age of discoveries”, yet advances in technology means that 98% of the data will not been seen by the human eye. In order to efficiently and effectively examine the data we are testing machine learning applications with crowd-sourced labels to locate these super-massive black holes. I show where machine learning is accelerating astronomical studies of super-massive black holes. I will also indicate the area where machine learning methods have the potential to lead the way in this new era of radio astronomy.