The merger fraction of post-starburst galaxies in UNIONS

Abstract

Post-starburst (PSB) galaxies 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 post-starburst galaxies have undergone a merger in their recent past.

Galaxy mergers can rapidly shut down star formation

“…Following previous work (e.g. Goto 2007;Pawlik et al 2018;Wilkinson et al 2022), in order to ensure adequate signalto-noise (S/N) to permit a robust post-starburst classification, we further require a g-band S/N > 8 in the SDSS spectrum, which reduces the sample to 99,703 galaxies. Finally, since the CNN (see below) used to identify post-merger galaxies in CFIS (Bickley et al 2021) was trained using simulated images of galaxies with stellar masses M > 10 10 M , we likewise apply this mass cut to the sample.…”

“…We measure quantitative morphologies for the entire CFIS sample using the publicly available Statmorph code (Rodriguez-Gomez et al 2019). The preparation of the images, object definition, masking procedures and all relevant details pertaining to the morphological parameterization are discussed in detail in Wilkinson et al (2022). In the work presented here, we make use of the shape asymmetry parameter (Pawlik et al 2016).…”

“…The nature of post-starbursts, and their connection to galaxy mergers, has been extensively stud-ied (e.g. Zabludoff et al 1996;Goto 2005;Pawlik et al 2016;Wilkinson et al 2022). These studies have demonstrated that a significant fraction of post-starbursts exhibit disturbed morphologies, with a merger fraction several times in excess of the expectations from a control sample (see Wilkinson et al 2022 for a comprehensive breakdown of post-starburst merger fractions using a variety of selection metrics).…”

“…Zabludoff et al 1996;Goto 2005;Pawlik et al 2016;Wilkinson et al 2022). These studies have demonstrated that a significant fraction of post-starbursts exhibit disturbed morphologies, with a merger fraction several times in excess of the expectations from a control sample (see Wilkinson et al 2022 for a comprehensive breakdown of post-starburst merger fractions using a variety of selection metrics). The high fraction (and excess) of merger activity amongst the post-starburst population demonstrates that merging is one potential avenue to quenching.…”

Galaxy mergers can rapidly shut down star formation

“…In contrast to the traditional asymmetry metric (which sums the residual flux in a 180-degree rotated image of the galaxy subtracted from the original; Conselice et al 2000), shape asymmetry uses a binary mask, in order to enhance the impact of faint tidal features. Although shape asymmetry (as well as other metrics such as traditional asymmetry and Gini-M20) can be used independently to identify mergers, CNN have been found to perform at a superior level when identifying post-merger samples (Bickley et al 2021;Wilkinson et al 2022). In the work presented here, we therefore use shape asymmetry only as a metric of the extent of structural disturbance, rather than for identifying the initial sample of postmergers.…”