Galaxy Evolution with Manifold Learning

TL;DR

This paper uses data science techniques to uncover how galaxies evolved over billions of years by analyzing their luminosities and cosmic time.

Contribution

The novel approach applies manifold learning to galaxy data, revealing a low-dimensional structure governed by star formation and stellar mass evolution.

Findings

Galaxy evolution in luminosity space is captured by a low-dimensional nonlinear structure called the galaxy manifold.

Two parameters, star formation and stellar mass evolution, effectively describe galaxy evolution on the manifold.

Manifold coordinates can potentially be linked to physical quantities, offering new insights into galaxy physics.

Abstract

Matter in the early Universe was nearly uniform, and galaxies emerged through the gravitational growth of small primordial density fluctuations. Astrophysics has been trying to unveil the complex physical phenomena that have caused the formation and evolution of galaxies throughout the 13-billion-year history of the Universe using the first principles of physics. However, since present-day astrophysical big data contain more than 100 explanatory variables, such a conventional methodology faces limits in dealing with such data. We, instead, elucidate the physics of galaxy evolution by applying manifold learning, one of the latest methods of data science, to a feature space spanned by galaxy luminosities and cosmic time. We discovered a low-dimensional nonlinear structure of data points in this space, referred to as the galaxy manifold. We found that the galaxy evolution in the ultraviolet–optical–near-infrared luminosity space is well described by two parameters, star formation and stellar mass evolution, on the manifold. We also discuss a possible way to connect the manifold coordinates to physical quantities.

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