Author: matlas

Ultra diffuse galaxies, characterized by their low surface brightness and large physical size, constitute a subclass of dwarf galaxies that challenge our current understanding of galaxy formation and evolution. In this paper, we probe the properties of 74 UDGs, identified in the MATLAS survey, based on a comprehensive study of their globular cluster (GC) populations. We obtained high resolution HST imaging of these galaxies using the ACS F606W and F814W filters, allowing us to select GCs based on color and concentration index.

Continue reading “Dwarf Galaxies in the MATLAS Survey: Hubble Space Telescope Observations of the Globular Cluster Systems of 74 Ultra Diffuse Galaxies”

We explore the use of deep learning to localise galactic structures in low surface brightness (LSB) images. LSB imaging reveals many interesting structures, though these are frequently confused with galactic dust contamination, due to a strong local visual similarity. We propose a novel unified approach to multi-class segmentation of galactic structures and of extended amorphous image contaminants. Our panoptic segmentation model combines Mask R-CNN with a contaminant specialised network and utilises an adaptive preprocessing layer to better capture the subtle features of LSB images.

Continue reading “Panoptic Segmentation of Galactic Structures in LSB Images”

In this paper, we address the challenge of segmenting global contaminants in large images. The precise delineation of such structures requires ample global context alongside understanding of textural patterns. CNNs specialise in the latter, though their ability to generate global features is limited. Attention measures long range dependencies in images, capturing global context, though at a large computational cost. We propose a gridded attention mechanism to address this limitation, greatly increasing efficiency by processing multi-scale features into smaller tiles.

Continue reading “Multi-scale gridded Gabor attention for cirrus segmentation (associated paper)”

Context. The rapidly improving quality and resolution of both low surface brightness observations and cosmological simulations of galaxies enable us to address the important question of how the formation history is imprinted in the outer unrelaxed regions of galaxies, and to inspect the correlations of these imprints with another tracer of galaxy formation, the internal kinematics.
Aims: Using the hydrodynamical cosmological simulation called Magneticum Pathfinder, we identified tidal tails, shells, streams, and satellite planes, and connected them to the amount of rotational support and the formation histories of the host galaxies. This presents the first combined statistical census considering all these four types of features in hydrodynamical cosmological simulations.
Methods: Tidal features were visually classified from a three-dimensional rendering of the simulated galaxies by several scientists independently. Only features that were identified by at least half of the participating individuals were considered to be existing features. The data on satellite planes and kinematic properties of the simulated galaxies were taken from previous work. The results were compared to observations, especially from the MATLAS survey.

Continue reading “A stream come true: Connecting tidal tails, shells, streams, and planes with galaxy kinematics and formation history”

Context. Dwarf galaxy abundances can serve as discernment tests for models of structure formation. Previous small-scale tensions between observations and dark matter-only cosmological simulations may have been resolved with the inclusion of baryonic processes; however, these successes have been largely concentrated on the Local Group dwarfs the feedback models were initially calibrated on.
Aims: We investigate whether the ΛCDM model can reliably reproduce dwarf abundances in the MATLAS low-to-moderate density fields that are centred upon early-type host galaxies beyond the Local Volume.

Continue reading “A too-many dwarf satellite galaxies problem in the MATLAS low-to-moderate density fields”