Dr Becky Smethurst

Abstract:

Recent observational and theoretical studies have suggested that supermassive black holes (SMBHs) grow mostly through non-merger (‘secular’) processes. Since galaxy mergers lead to dynamical bulge growth, the only way to observationally isolate non-merger growth is to study galaxies with low bulge-to-total mass ratio (e.g. B/T < 10 per cent). However, bulge growth can also occur due to secular processes, such as disc instabilities, making disc-dominated selections a somewhat incomplete way to select merger-free systems. Here we use the Horizon-AGN simulation to select simulated galaxies which have not undergone a merger since z = 2, regardless of bulge mass, and investigate their location on typical black hole-galaxy scaling relations in comparison to galaxies with merger dominated histories. While the existence of these correlations has long been interpreted as co-evolution of galaxies and their SMBHs driven by galaxy mergers, we show here that they persist even in the absence of mergers. We find that the correlations between SMBH mass and both total mass and stellar velocity dispersion are independent of B/T ratio for both merger-free and merger-dominated galaxies. In addition, the bulge mass and SMBH mass correlation is still apparent for merger-free galaxies, the intercept for which is dependent on B/T. Galaxy mergers reduce the scatter around the scaling relations, with merger-free systems showing broader scatter. We show that for merger-free galaxies, the co-evolution is dominated by radio-mode feedback, and suggest that the long periods of time between galaxy mergers make an important contribution to the co-evolution between galaxies and SMBHs in all galaxies.

Abstract:

Abstract Mergers play a complex role in galaxy formation and evolution. Continuing to improve our understanding of these systems requires ever larger samples, which can be difficult (even impossible) to select from individual surveys. We use the new platform ESA Datalabs to assemble a catalog of interacting galaxies from the Hubble Space Telescope science archives; this catalog is larger than previously published catalogs by nearly an order of magnitude. In particular, we apply the Zoobot convolutional neural network directly to the entire public archive of HST F814W images and make probabilistic interaction predictions for 126 million sources from the Hubble Source Catalog. We employ a combination of automated visual representation and visual analysis to identify a clean sample of 21,926 interacting galaxy systems, mostly with z < 1. Sixty-five percent of these systems have no previous references in either the NASA Extragalactic Database or Simbad. In the process of removing contamination, we also discover many other objects of interest, such as gravitational lenses, edge-on protoplanetary disks, and “backlit” overlapping galaxies. We briefly investigate the basic properties of this sample, and we make our catalog publicly available for use by the community. In addition to providing a new catalog of scientifically interesting objects imaged by HST, this work also demonstrates the power of the ESA Datalabs tool to facilitate substantial archival analysis without placing a high computational or storage burden on the end user.