  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 study how galaxies
distribute based on their resolved and global physical properties. We show that
even when using exclusively information about the internal structure, galaxies
naturally cluster into two well-known categories from a purely data driven
perspective: rotating main-sequence disks and massive slow rotators, hence
confirming distinct assembly channels. Low-mass rotation-dominated quenched
galaxies appear only as a third cluster if information about the integrated
physical properties is preserved, suggesting a mixture of assembly processes
for these galaxies without any particular signature in their internal
kinematics that distinguishes them from the two main groups. The framework for
data exploration is publicly released with this publication, ready to be used
with the MaNGA or other integral field data sets.
