Author: matlas

We present a photometric study of the dwarf galaxy population in the low to moderate density environments of the MATLAS deep imaging survey. The sample consists of 2210 dwarfs, including 508 nucleated. We define a nucleus as a compact source that is close to the galaxy photocenter (within 0.5 ReRe) which is also the brightest such source within the galaxy’s effective radius. The morphological analysis is performed using a two-dimensional surface brightness profile modeling on the g-band images of both the galaxies and nuclei. 

Continue reading “Structure and morphology of the MATLAS dwarf galaxies and their central nuclei”

Context. A small fraction of early-type galaxies (ETGs) show prolate rotation, i.e. they rotate around their long photometric axis. In simulations, certain configurations of galaxy mergers are known to produce this type of rotation.
Aims. We investigate the association of prolate rotation and signs of (past) galaxy interactions among the observed galaxies.

Continue reading “Ubiquitous signs of interactions in early-type galaxies with prolate rotation”

Robustness to transformation is desirable in many computer vision tasks, given that input data often exhibits pose variance within classes. While translation invariance and equivariance is a documented phenomenon of CNNs, sensitivity to other transformations is typically encouraged through data augmentation. We investigate the modulation of complex valued convolutional weights with learned Gabor filters to enable orientation robustness. With Gabor modulation, the designed network is able to generate orientation dependent features free of interpolation with a single set of rotation-governing parameters. Moreover, by learning rotation parameters alongside traditional convolutional weights, the representation space is not constrained and may adapt to the exact input transformation. We present Learnable Convolutional Gabor Networks (LCGNs), that are parameter-efficient and offer increased model complexity while keeping backpropagation simple. We demonstrate that learned Gabor modulation utilising an end-to-end complex architecture enables rotation invariance and equivariance on MNIST and a new dataset of simulated images of galactic cirri.

Richards et al., 2020, Arxiv

Deep imaging, that is imaging capable of capturing very low surface brightness extended objects, is a quickly growing field of extragalactic astronomy. Not only can new types of faint objects be discovered, but deep images of bright galaxies are very valuable, too, since they reveal faint signs of past galaxy collisions, the tidal features. Such “archeological” record can be exploited for investigating how galaxies formed. In the MATLAS survey, we obtained extremely deep images of 177 nearby massive elliptical and lenticular galaxies using the 3.5m Canada-France-Hawaii Telescope.

Continue reading “The MATLAS survey of faint outskirts of bright galaxies”

Accounting for the morphology is an essential constraint for the theory of galaxy formation. The low-surface brightness regions of galaxies host multiple morphological features that any realistic model should consider; however they are still not fully explored because of observational difficulties. Here we present the results of our visual inspections of very deep images of a complete large volume-limited sample of 177 nearby massive early-type galaxies (ETGs) from the MATLAS survey.

Continue reading “Census and classification of low-surface-brightness structures in nearby early-type galaxies from the MATLAS survey”