Related papers: Identifying Galaxy Cluster Mergers with Deep Neura…
We present a new application of deep learning to reconstruct the cosmic microwave background (CMB) temperature maps from the images of microwave sky, and to use these reconstructed maps to estimate the masses of galaxy clusters. We use a…
Machine-learning methods are increasingly applied to astronomical surveys, providing powerful tools for detecting and studying galaxy clusters. We investigate the mass-redshift properties and completeness of the ComPACT galaxy cluster…
We measure the fraction of galaxy-galaxy mergers in two clusters at $z\sim2$ using imaging and grism observations from the {\it Hubble Space Telescope}. The two galaxy cluster candidates were originally identified as overdensities of…
We present the results of a simple but robust morphological classification of a statis- tically complete sample of 108 of the most X-ray luminous clusters at 0.15 < z < 0.7 observed with Chandra. Our aims are to (a) identify the most…
Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations,…
Significant galaxy mergers throughout cosmic time play a fundamental role in theories of galaxy evolution. The widespread usage of human classifiers to visually assess whether galaxies are in merging systems remains a fundamental component…
The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…
We present image-based evolution of galaxy mergers from the Illustris cosmological simulation at 12 time-steps over 0.5 < z < 5. To do so, we created approximately one million synthetic deep Hubble Space Telescope and James Webb Space…
Galaxy mergers are crucial for understanding galaxy evolution, and with large upcoming datasets, automated methods such as Convolutional Neural Networks (CNNs) are essential for efficient detection. It is understood that CNNs classify…
We present an automatic method to identify galaxy mergers using the morphological information contained in the residual images of galaxies after the subtraction of a Sersic model. The removal of the bulk signal from the host galaxy light is…
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizing machine learning techniques to perform clustering and regression tasks. We examine five…
A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task which suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic…
In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep…
Clusters of galaxies mass can be inferred by indirect observations, see X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation…
Using the data set of The Three Hundred project, i.e. a suite of 324 hydrodynamical resimulations of cluster-sized haloes, we study galaxy cluster mergers and their effect on colour and luminosity changes of their brightest cluster galaxies…
Strong gravitational lensing by irregular mass distributions, such as galaxy clusters, is generally not well quantified by cross sections of analytic mass models. Computationally expensive ray-tracing methods have so far been necessary for…
Galaxy clusters, the most massive, dark-matter-dominated, and most recently assembled structures in the Universe, are key tools for probing cosmology. However, uncertainties in scaling relations that connect cluster mass to observables like…
We present a very large high-resolution cosmological N-body simulation, the Millennium-XXL or MXXL, which uses 303 billion particles to represent the formation of dark matter structures throughout a 4.1Gpc box in a LambdaCDM cosmology. We…
Galaxy clusters grow through the accretion of galaxies from groups, filaments, and other clusters. During this process, galaxies may undergo pre-processing in lower-density environments, where galaxy-galaxy mergers and other interactions…
We aim to generate a catalogue of merging galaxies within the 5.4 sq. deg. North Ecliptic Pole over the redshift range $0.0 < z < 0.3$. To do this, imaging data from the Hyper Suprime-Cam are used along with morphological parameters derived…