Related papers: A Deep Learning Approach for Characterizing Major …
We present a deep learning model to predict the r-band bulge-to-total light ratio (B/T) of nearby galaxies using their multi-band JPEG images alone. Our Convolutional Neural Network (CNN) based regression model is trained on a large sample…
We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs). We discuss the statistical background of approximate Bayesian neural networks and…
Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land…
Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…
We derive the number of dynamically close companions per galaxy ($N_c$) and their total luminosity ($L_c$) for galaxies in the Millennium Galaxy Catalogue: $N_c$ is similar to the fraction of galaxies in close pairs and is directly related…
The majority of massive star-forming galaxies at $z\sim2$ have velocity gradients suggestive of rotation, in addition to large amounts of disordered motions. In this paper, we demonstrate that it is challenging to distinguish the regular…
Galaxy mergers are expected to have a significant role in the mass assembly of galaxies in the early Universe, but there are very few observational constraints on the merger history of galaxies at $z>2$. We present the first study of galaxy…
The evolution of our Universe is strongly influenced by the attractive force of gravity. A key aspect of this evolution, therefore, is the merging of galaxies. Here, we explore the role of mergers in shaping the properties of massive…
We use the Galaxy And Mass Assembly (GAMA) survey to measure the local Universe mass dependent merger fraction and merger rate using galaxy pairs and the CAS structural method, which identifies highly asymmetric merger candidate galaxies.…
The observation of our home galaxy, the Milky Way (MW), is made difficult by our internal viewpoint. The Gaia survey that contains around 1.6 billion star distances is the new flagship of MW structure and can be combined with other…
We compare mass-selected close pairs at z > 1 with the intrinsic galaxy merger rate in the Illustris Simulations. To do so, we construct three 140 arcmin^2 lightcone catalogs and measure pair fractions, finding that they change little or…
Upcoming deep imaging surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time will be confronted with challenges that come with increased depth. One of the leading systematic errors in deep surveys is the blending of…
A fundamental prediction of the LambdaCDM cosmology is the hierarchical build-up of structure and therefore the successive merging of galaxies into more massive ones. As one can only observe galaxies at one specific time in cosmic history,…
Many recent observations and numerical simulations suggest that nearby massive, early-type galaxies were formed through a "two-phase" process. In the proposed second phase, the extended stellar envelope was accumulated through many dry…
Numerical simulations predict that minor mergers are an important channel for the mass assembly of galaxies. However, minor mergers are relatively difficult to detect using imaging, especially at high redshift. While such events are much…
We present a novel machine learning framework tailored to detect massive black hole binaries observed by spaceborne gravitational wave detectors like the Laser Interferometer Space Antenna (LISA) and predict their future merger times. The…
Merging galaxy clusters may provide a unique window into the behavior of dark matter and the evolution of member galaxies. To interpret these natural collider experiments we must account for how much time has passed since pericenter passage…
We explore how information in images of nearby galaxies can be used to estimate their distance. We train a convolutional Neural Network (NN) to do this, using galaxy images from the Illustris simulation. We show that if the NN is trained on…
In this review we discuss the evidence for galaxy interactions and mergers in the distant universe and the role of mergers in forming galaxies. Observations show that the fraction of massive (M> M_*) galaxies involved in major mergers is…
Imaging through scattering is an important, yet challenging problem. Tremendous progress has been made by exploiting the deterministic input-output "transmission matrix" for a fixed medium. However, this "one-to-one" mapping is highly…