Galaxy classification via machine learning
To understand how galaxies grow, by component or as a whole,
Research Field
and whether galaxies evolve in two phases (i.e. spheroid
Radio Astronomy
formation, and disk growth) single component fitting and bulge
Curtin Institute for Computation
and disc decomposition (i.e. fitting the light profile of the inner and
outer components simultaneously) needs to be performed in a Project Suitability
PhD
comprehensive manner starting in the local universe and slowly
increasing in redshift. A major problem faced when doing this
Project Supervisor
structural decomposition on large samples of galaxies is the
TBC
identification of multi-component systems. There are two
possibilities to deal with this, i) use morphological classification as
a prior and only fit galaxies identified as multi-component systems
Co-Supervisors
with multiple light profiles, ii) fit all galaxies blindly with single and
Kevin Chai
Rebecca Lange
multi-component light profiles and use logical filters and other
criteria (e.g. BIC or AIC) after the fact to find the multi-component
systems. The first option is preferred since it is computationally less expensive, however, the
morphological classification can be difficult. Having robust morphology classifications is not only
important for fitting the light profiles of galaxies but also to understand how the relative fractions of
various galaxy types change over cosmic time.
Currently the morphology of galaxies is typically es tablished by visual classification, either by
astronomers or through citizen science projects like Galaxy Zoo. However, this is mostly restricted to
lower redshifts and we are reaching the limit of the number of galaxies that can easily be classified this
way.
Establishing a tool to classify galaxies is especially important in the era of the upcoming new ground
and space-based telescopes (such as the Euclid mission, the Wide Field InfraRed survey Telescope WFIRST, the James Webb Space telescope - JWST, the Giant Magellan Telescope - GMT to name a
few) which will enable us to probe larger areas of sky with better resolution, probing deeper towards the
beginning of galaxy assembly increasing the number of known redshifts into the billions and collecting
high resolution imaging for a sizable fraction of them.
Machine learning is arguably a suitable approach for classifying large datasets of galaxy images. State
of the art machine learning algorithms (deep learning) have achieved good results in recent years
particularly on image classification tasks. Additionally, feature extraction is used in machine learning to
automatically extract information from the raw pixels of images rather than requiring domain knowledge
to engineer features such as the light profile fit of a galaxy for classification. Automatically extracted
features could produce better classification results than manually engineered features. Therefore, this
project explores the feasibility of using deep learning algorithms (e.g. convolutional neural networks) to
classify galaxies based on their morphology.
This will be a project involving research across the Curtin University’s Institute of Radio
Astronomy and the Institute for Computation: it will develop cutting-edge computational
techniques with access to expertise and supercomputing resources.