The merger fraction of post-starburst galaxies in UNIONS

Abstract

Post-starburst galaxies (PSBs) are defined as having experienced a recent burst of star formation, followed by a prompt truncation in further activity. Identifying the mechanism(s) causing a galaxy to experience a post-starburst phase therefore provides integral insight into the causes of rapid quenching. Galaxy mergers have long been proposed as a possible post-starburst trigger. Effectively testing this hypothesis requires a large spectroscopic galaxy survey to identify the rare PSBs as well as high-quality imaging and robust morphology metrics to identify mergers. We bring together these critical elements by selecting PSBs from the overlap of the Sloan Digital Sky Survey and the Canada–France Imaging Survey and applying a suite of classification methods: non-parametric morphology metrics such as asymmetry and Gini-M20, a convolutional neural network trained to identify post-merger galaxies, and visual classification. This work is therefore the largest and most comprehensive assessment of the merger fraction of PSBs to date. We find that the merger fraction of PSBs ranges from 19 per cent to 42 per cent depending on the merger identification method and details of the PSB sample selection. These merger fractions represent an excess of 3–46× relative to non-PSB control samples. Our results demonstrate that mergers play a significant role in generating PSBs, but that other mechanisms are also required. However, applying our merger identification metrics to known post-mergers in the IllustrisTNG simulation shows that 70 per cent of recent post-mergers (≲200 Myr) would not be detected. Thus, we cannot exclude the possibility that nearly all PSBs have undergone a merger in their recent past.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 20, 2022
Source ID
10.1093/mnras/stac1962

Entities

People

  • Connor Bottrell
  • Jean‐Charles Cuillandre
  • Robert W. Bickley
  • Sara Ellison
  • Scott Wilkinson
  • Stephen Gwyn
  • Vivienne Wild

Organizations

  • Alfred P. Sloan Foundation
  • American Museum of Natural History
  • California Earthquake Authority
  • Canadian Space Agency
  • Case Western Reserve University
  • Chinese Academy of Sciences
  • Drexel University
  • Higher Education Funding Council for England
  • Institute for Advanced Study
  • Johns Hopkins University
  • Los Alamos National Laboratory
  • Maritime and Port Authority of Singapore
  • Max Planck Society
  • National Aeronautics and Space Administration
  • National Astronomical Observatory of Japan
  • National Center for Scientific Research
  • National Research Council Canada
  • National Science Foundation
  • Natural Sciences and Engineering Research Council
  • New Mexico State University
  • Ohio State University
  • Princeton University
  • Science and Technology Facilities Council
  • Society of Surgical Oncology
  • United States Department of Energy
  • United States Naval Observatory
  • University of Basel
  • University of Cambridge
  • University of Chicago
  • University of Hawaiʻi System
  • University of Pittsburgh
  • University of Portsmouth
  • University of St Andrews
  • University of Victoria
  • University of Washington

Tags

Fields of Study

  • Physics

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Astronomy/Astrophysics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks