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

TL;DR

This paper introduces a novel self-supervised machine learning approach to visualize and analyze the complex, multi-dimensional data of MaNGA galaxies, revealing natural clustering into known galaxy types based on internal physical properties.

Contribution

The study presents a new self-supervised learning framework that effectively captures physical galaxy properties and clusters galaxies without bias from observational parameters, validated on MaNGA data.

Findings

Galaxies naturally cluster into rotating disks and slow rotators based on internal structure.

The method confirms known galaxy categories using purely data-driven internal features.

A third cluster of low-mass quenched galaxies suggests diverse assembly processes.

Abstract

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.