Related papers: Bridging observations and simulations: a machine l…
Clusters of galaxies, the most massive virialized systems known, provide a powerful tool for studying the structure, the mass density, and the cosmology of our universe. Clusters furnish one of the best estimates of the dynamical mass…
The alignment of clusters of galaxies with their nearest neighbours and between clusters within a supercluster is investigated using simulations of 512^{3} dark matter particles for \LambdaCDM and \tauCDM cosmological models. Strongly…
This is the second paper in a series presenting the results from a 500 $h^{-1}$Mpc large constrained hydro-dynamical simulation of the local Universe (SLOW). The initial conditions are based on peculiar velocities derived from the…
This paper demonstrates that the stellar masses of galaxies in the Galaxy and Mass Assembly (GAMA) survey, originally derived via stellar population synthesis modelling, can be accurately predicted using only their absolute magnitudes and…
We describe the construction of catalogues of galaxy clusters from the APM Galaxy survey using an automated algorithm based on Abell-like selection criteria. We investigate the effects of varying several parameters in our selection…
As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important. In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of…
Studies of galaxy clusters have proved crucial in helping to establish the standard model of cosmology, with a universe dominated by dark matter and dark energy. A theoretical basis that describes clusters as massive, multi-component,…
Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of…
The LMC's Globular Clusters (GCs) bring a novel opportunity to understand the LMC's assembly history and dark matter (DM) properties, provided the kinematically outlying GCs can be reliably identified. However, traditional diagnostics like…
This paper presents a deep relational metric learning (DRML) framework for image clustering and retrieval. Most existing deep metric learning methods learn an embedding space with a general objective of increasing interclass distances and…
Merging galaxy clusters allows to study the different mass components, dark and baryonic, separately. Also their occurrence enables to test the $\Lambda$CDM scenario and they could put constrains in the self interacting cross section of the…
Galaxy clusters, the pinnacle of structure formation in our universe, are a powerful cosmological probe. Several approaches have been proposed to express cluster number counts, but all these methods rely on empirical explicit scaling…
The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past merger events. While the majority of galaxies in the local…
Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We…
In spite of the great progress in human motion prediction, it is still a challenging task to predict those aperiodic and complicated motions. We believe that to capture the correlations among human body components is the key to understand…
The mass measurement of galaxy clusters is an important tool for the determination of cosmological parameters describing the matter and energy content of the Universe. However, the standard methods rely on various assumptions about the…
We present the "Galaxy Cluster Merger Catalog." This catalog provides an extensive suite of mock observations and related data for N-body and hydrodynamical simulations of galaxy cluster mergers and clusters from cosmological simulations.…
We introduce intra-class memorability, where certain images within the same class are more memorable than others despite shared category characteristics. To investigate what features make one object instance more memorable than others, we…
We present a random forest framework for predicting circumgalactic medium (CGM) physical conditions from quasar absorption line observables, trained on a sample of Voigt profile-fit synthetic absorbers from the Simba cosmological…
Cold dark matter (CDM) constitutes most of the matter in the Universe. The interplay between dark and luminous matter in dense cosmic environments like galaxy clusters is studied theoretically using cosmological simulations. Observed…