Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Capturing the Physics of MaNGA Galaxies with Self-supervised Machine Learning

Sarmiento, R, Huertas-Company, M, Knapen, JH, Sanchez, SF, Dominguez Sanchez, H, Drory, N and Falcon-Barroso, J (2021) Capturing the Physics of MaNGA Galaxies with Self-supervised Machine Learning. ASTROPHYSICAL JOURNAL, 921 (2). ISSN 0004-637X

2104.08292v2.pdf - Accepted Version

Download (5MB) | Preview


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 high dimensionality and complex data sets, which challenges 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 multidimensional information on stellar population and kinematics in the MaNGA survey in a 2D plane. Our framework is insensitive to nonphysical properties such as the size of the integral field unit 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 exclusively using information about the internal structure, galaxies naturally cluster into two well-known categories, rotating main-sequence disks and massive slow rotators, from a purely data-driven perspective, hence confirming distinct assembly channels. Low-mass rotation-dominated quenched galaxies appear as a third cluster only 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.

Item Type: Article
Uncontrolled Keywords: Astronomy & Astrophysics; LIBRARY; Physical Sciences; PROJECT; Science & Technology; SDSS; STAR-FORMATION; STELLAR ANGULAR-MOMENTUM; TELESCOPE; Science & Technology; Physical Sciences; Astronomy & Astrophysics; STELLAR ANGULAR-MOMENTUM; STAR-FORMATION; SDSS; TELESCOPE; PROJECT; LIBRARY; astro-ph.GA; astro-ph.GA; Astronomy & Astrophysics; 0201 Astronomical and Space Sciences; 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics; 0306 Physical Chemistry (incl. Structural)
Subjects: Q Science > QB Astronomy
Divisions: Astrophysics Research Institute
Publisher: IOP Publishing Ltd
SWORD Depositor: A Symplectic
Date Deposited: 10 Oct 2022 15:55
Last Modified: 12 Nov 2022 00:50
DOI or ID number: 10.3847/1538-4357/ac1dac
URI: https://researchonline.ljmu.ac.uk/id/eprint/17793
View Item View Item