Related papers: Automatic quantitative morphological analysis of i…
Galaxy mergers, the dynamical process during which two galaxies collide, are among the most spectacular phenomena in the Universe. During this process, the two colliding galaxies are tidally disrupted, producing significant visual features…
Merging is potentially the dominate process in galaxy formation, yet there is still debate about its history over cosmic time. To address this we classify major mergers and measure galaxy merger rates up to z $\sim$ 3 in all five CANDELS…
We have undertaken a large set of simulations of galaxy interactions and mergers (GalMer Project) in order to study the physical processes related to galaxy encounters. All morphological types along the Hubble sequence are considered in the…
Modern astronomy relies on massive databases collected by robotic telescopes and digital sky surveys, acquiring data in a much faster pace than what manual analysis can support. Among other data, these sky surveys collect information about…
The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS)…
We present cross sections and reaction rates for merging to occur during encounters of equal-mass spherical galaxies. As an application, we determine the rate of galaxy merging in clusters of galaxies. We present results for two types of…
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.…
Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented…
Galaxy morphology is a key parameter in galaxy evolution studies. The enormous number of galaxies which current and future surveys will observe demand of automated methods for morphological classification. Supervised learning techniques…
Classification of galaxy morphology is a challenging but meaningful task for the enormous amount of data produced by the next-generation telescope. By introducing the adaptive polar coordinate transformation, we develop a rotationally…
We present a study of the dynamics of multi-component models of spiral galaxies at various stages of grand merging. Numerical models include a self-consistent account of the dynamics of collisionless stellar subsystems and N-body dark…
We present the largest, most homogeneous catalogue of merging galaxies in the nearby universe obtained through the Galaxy Zoo project - an interface on the world-wide web enabling large-scale morphological classification of galaxies through…
The science behind galaxy interaction and mergers has a fundamental role and gives us an insight into galaxy formation and its evolution. Fluctuating angular momentum is responsible for extraordinary events like polar rings, tidal tails,…
To determine the importance of merging galaxies to galaxy evolution, it is necessary to design classification tools that can identify different types and stages of merging galaxies. Previously, using GADGET-3/SUNRISE simulations of merging…
Encoded within the morphological structure of galaxies are clues related to their formation and evolutionary history. Recent advances pertaining to the statistics of galaxy morphology include sophisticated measures of concentration (C),…
The distribution of galaxy morphological types is a key test for models of galaxy formation and evolution, providing strong constraints on the relative contribution of different physical processes responsible for the growth of the…
Merging galaxies play a key role in galaxy evolution, and progress in our understanding of galaxy evolution is slowed by the difficulty of making accurate galaxy merger identifications. We use GADGET-3 hydrodynamical simulations of merging…
We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS…
We train three convolutional neural networks (CNNs) to classify galaxies with Galaxy Zoo 2 dataset and extract the activations from the last fully connected layer or the last average pooling layer of CNNs to study the high-dimensional…
Theoretical models are vital for exploring the galaxy merger process, which plays a crucial role in the evolution of galaxies. Recent advances in modelling have placed tight constraints on the buildup of stellar material in galaxies across…