Capturing the physics of MaNGA galaxies with self-supervised Machine Learning

Understand

As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important.

• In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of increasing dimensionality and complexity of datasets, which challenge the traditional methods used to extract the physical information they contain.
• We present the use of a novel self-supervised Machine Learning method to visualize the multi-dimensional information on stellar population and kinematics in the MaNGA survey in a two dimensional plane.
• Our framework is insensitive to non-physical properties such as the size of integral field unit (IFU) and is therefore able to order galaxies according to their resolved physical properties.