Unveiling hidden mergers: quantifying merger prevalence in post-starburst galaxies and the efficacy of common merger identification techniques
Date
2023-04-24
Authors
Wilkinson, Scott
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Abstract
Numerical simulations and observations agree that galaxies tend to evolve from star-forming spiral galaxies to quiescent ellipticals. Post-starburst (PSB) galaxies, defined as having experienced a recent burst of star formation, followed by a prompt truncation in further activity, are thought to be observed whilst rapidly transitioning from star-forming to quiescence. Thus, identifying the mechanism(s) causing a galaxy to experience a post-starburst phase 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. In this work, I 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 including non-parametric morphology metrics such as asymmetry and Gini-M20, a convolutional neural network (CNN) trained to identify post-merger galaxies, and visual classification. This work therefore includes the largest and most comprehensive assessment of the merger fraction of PSBs to date. I find that the merger fraction of PSBs ranges from 19% to 42% depending on the merger identification method and details of the PSB sample selection. These merger fractions represent an excess of 3-46x relative to non-PSB control samples. My results demonstrate that mergers play a significant role in generating PSBs, but that other mechanisms are also required.
Critical to the interpretation of the observed merger fraction of PSBs in this work is quantifying the efficacy of the merger identification methods employed. To test this, 2,119 known recent mergers (< 200 Myr) are drawn from the IllustrisTNG100 cosmological simulation, where assembly history and properties of the merger are known with certainty and devoid of observational impairment. Synthetic r-band images of the mergers are generated directly from the simulation particle data and degraded to various image qualities, adding observational effects such as sky noise and atmospheric blurring. The efficacy of the non-parametric merger identification methods is quantified using the completeness of recovered mergers, which is shown to increase from as low as 1% to as high as 37% as sky noise and atmospheric blurring decrease, but also depends on the morphology statistic considered. However, even in idealized imaging, free from atmospheric blurring and with minimal sky noise, the maximum completeness achieved is 39% indicating that reliable merger detection may be limited by the viewing angle at which it is observed. Future work will assess if CNN and visual inspection methods can identify mergers more reliably and thus allow for a more accurate quantification of the number of mergers in post-starburst galaxies and other galaxy samples, moving forward.
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Keywords
Galaxies, Astrophysics, Morphology, Galaxy Mergers and Interactions, Observational Astronomy, Cosmological Simulations, Galaxy Evolution, Merger Identification Techniques, Synthetic Imaging, Observational Realism