Related papers: SubDLe: identification of substructures in cosmolo…
Continuous wavelet analysis is gaining popularity in science and engineering for its ability to analyze data across spatial and scale domains simultaneously. In this study, we introduce a wavelet-based method to identify halos and assess…
To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double S\'ersic profile, neglecting the influence of galaxy substructures and morphologies deviating from such…
Deep convolutional neural networks achieve remarkable visual recognition performance, at the cost of high computational complexity. In this paper, we have a new design of efficient convolutional layers based on three schemes. The 3D…
Identifying galaxies in hydrodynamical simulations is a difficult task, particularly in regions of high density such as galaxy groups and clusters. We present a new scale-free shape-independent algorithm to robustly and accurately identify…
We present AstroLink, an efficient and versatile clustering algorithm designed to hierarchically classify astrophysically-relevant structures from both synthetic and observational data sets. We build upon CluSTAR-ND, a hierarchical…
Cosmological N-body simulations represent an excellent tool to study the formation and evolution of dark matter (DM) halos and the mechanisms that have originated the universal profile at the largest mass scales in the Universe. In…
Can N-body simulations reliably determine the structural properties of dark matter halos? Focussing on a Virgo-sized galaxy cluster, we increase the resolution of current ``high resolution simulations'' by almost an order of magnitude to…
A prediction of the standard LCDM cosmology is that dark matter (DM) halos are teeming with numerous self-bound substructure, or subhalos. The precise properties of these subhalos represent important probes of the underlying cosmological…
Subhaloes are critical in distinguishing dark matter models, yet their evolution within galactic haloes, particularly in the Fuzzy Dark Matter (FDM) model, remains challenging to fully investigate in numerical simulations. In this work, we…
We address the issue of numerical convergence in cosmological smoothed particle hydrodynamics simulations using a suite of runs drawn from the EAGLE project. Our simulations adopt subgrid models that produce realistic galaxy populations at…
Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…
We investigate the power of the caustic technique for identifying substructures of galaxy clusters from optical redshift data alone. The caustic technique is designed to estimate the mass profile of galaxy clusters to radii well beyond the…
Superpixel-based methodologies have become increasingly popular in computer vision, especially when the computation is too expensive in time or memory to perform with a large number of pixels or features. However, rarely is superpixel…
Structured documents analysis and recognition are essential for modern online on-boarding processes, and document localization is a crucial step to achieve reliable key information extraction. While deep-learning has become the standard…
In the past decade, surveys of the stellar component of the Galaxy have revealed a number of streams from tidally disrupted dwarf galaxies and globular clusters. Simulations of hierarchical structure formation in LCDM cosmologies predict…
We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming…
We present VELOCIraptor, a massively parallel galaxy/(sub)halo finder that is also capable of robustly identifying tidally disrupted objects and separate stellar halos from galaxies. The code is written in c++11, use the MPI and OpenMP…
The analysis of state-of-the-art cosmological surveys like the Dark Energy Spectroscopic Instrument (DESI) survey requires high-resolution, large-volume simulations. However, the computational cost of hydrodynamical simulations at these…
We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for…
Member galaxies within galaxy clusters nowadays can be routinely identified in cosmological, hydrodynamical simulations using methods based on identifying self bound, locally over dense substructures. However, distinguishing the central…