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

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.

17 pages, 9 figures. Accepted for publication in The Astrophysical Journal

References in corpus (12)

• Distilling the Knowledge in a Neural Network
• The 2.5 m Telescope of the Sloan Digital Sky Survey
• Overview of the SDSS-IV MaNGA Survey: Mapping Nearby Galaxies at Apache Point Observatory
• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
• The SAURON project -- IX. A kinematic classification for early-type galaxies
• Strangulation as the primary mechanism for shutting down star formation in galaxies
• The MaNGA Integral Field Unit Fiber Feed System for the Sloan 2.5 m Telescope
• The Data Reduction Pipeline for the SDSS-IV MaNGA IFU Galaxy Survey
• The Cosmic Evolution Survey (COSMOS): Subaru Observations of the HST COSMOS Field
• SDSS-IV MaNGA IFS Galaxy Survey --- Survey Design, Execution, and Initial Data Quality
• SDSS-IV MaNGA: Spatially resolved star formation histories in galaxies as a function of galaxy mass and type
• The CALIFA view on stellar angular momentum across the Hubble sequence

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