Author: matlas

It was first observed in the 1970s that the dwarf galaxies surrounding our Milky Way, so-called satellites, appear to be arranged in a thin, vast plane. Similar discoveries have been made around additional galaxies in the local Universe such as Andromeda, Centaurus A, and potentially M83. In the specific cases with available kinematic data, the dwarf satellites also appear to preferentially co-orbit their massive host galaxy. Planes of satellites are rare in the lambda cold dark matter (ΛΛCDM) paradigm, although they may be a natural consequence of projection effects. Such a phase-space correlation, however, remains difficult to explain. In this work we analyzed the 2D spatial distribution of 2210 dwarf galaxies around early-type galaxies (ETGs) in the low-to-medium density fields of the “Mass Assembly of early-Type GaLAxies with their fine Structures” (MATLAS) survey. 

Continue reading “Flattened structures of dwarf satellites around massive host galaxies in the MATLAS low-to-medium density fields”

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