  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. Using the extracted representations, we show that galaxies
naturally cluster into three well-known categories from a purely data driven
perspective: rotating main-sequence disks, massive slow rotators and low-mass
rotation-dominated quenched galaxies. The framework for data exploration is
publicly released with this publication, ready to be used with the MaNGA or
other integral field data sets.
