Related papers: Bridging observations and simulations: a machine l…
We present a new method to identify and characterize the structure of the intracluster medium (ICM) in simulated galaxy clusters. The method uses the median of gas properties, such as density and pressure, which we show to be very robust to…
We present a machine-learning approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep machine learning tool commonly used in image recognition tasks. The CNN is trained…
In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…
The cycling of material from the interstellar medium (ISM) into stars and the return of stellar ejecta into the ISM is the engine that drives the "galactic ecology" in normal spirals, a cornerstone in the formation and evolution of galaxies…
We describe the results of observations of galaxy clusters conducted with ISOCAM on-board the Infrared Space Observatory. Our research is aimed at understanding the processes driving galaxy evolution in dense environments, free from the…
Peculiar velocities of clusters of galaxies can be measured by studying the fluctuations in the cosmic microwave background (CMB) generated by the scattering of the microwave photons by the hot X-ray emitting gas inside clusters. While for…
While the concept of "clustering" primarily refers to galaxies, the fact is that galaxies can be neglected - at least in terms of mass - when considering the continuous competition between accretion and relaxation processes of dark matter…
We present a new approach based on Supervised Machine Learning (SML) algorithms to infer key physical properties of galaxies (density, metallicity, column density and ionization parameter) from their emission line spectra. We introduce a…
The outskirts of galaxy clusters, near the virial radius, remain relatively unexplored territory and yet are vital to our understanding of cluster growth, structure, and mass. In this presentation, we show the first results from a program…
Distinguishing galaxies as either fast or slow rotators plays a vital role in understanding the processes behind galaxy formation and evolution. Standard techniques, which are based on the $\lambda_R$-spin parameter obtained from stellar…
We search for evidence of rotational support by analyzing the thermodynamic profiles of the intracluster medium (ICM) in a sample of nearby, massive galaxy clusters. For each object of the XMM-Newton Cluster Outskirts Project (X-COP)…
The number density of galaxy clusters across mass and redshift has been established as a powerful cosmological probe. Cosmological analyses with galaxy clusters traditionally employ scaling relations. However, many challenges arise from…
The formation, inner properties, and spatial distribution of galaxy groups and clusters are closely related to the background cosmological model. We use numerical simulations of variants of the CDM model with different cosmological…
Globular clusters (GCs) are powerful tracers of the galaxy assembly process, and have already been used to obtain a detailed picture of the progenitors of the Milky Way. Using the E-MOSAICS cosmological simulation of a (34.4 Mpc)$^3$ volume…
Many observable properties of globular clusters (GCs) provide valuable insights for unveiling the hierarchical assembly of their host galaxy. For the Milky Way (MW) in particular, GCs from different accreted satellite galaxies show distinct…
Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they…
We introduce IGM-Vis, a novel astrophysics visualization and data analysis application for investigating galaxies and the gas that surrounds them in context with their larger scale environment, the Cosmic Web. Environment is an important…
Understanding the formation and evolution of galaxy populations requires robust classification and characterisation techniques that jointly account for internal galaxy properties and environment. We analyse $5,306$ galaxies from the Galaxy…
We compare the distribution of internal velocity dispersions of galaxy clusters for an observational sample to those obtained from a set of N-body simulations of seven COBE-normalised cosmological scenarios: the standard CDM (SCDM) and a…
We propose In-Context Clustering (ICC), a flexible LLM-based procedure for clustering data from diverse distributions. Unlike traditional clustering algorithms constrained by predefined similarity measures, ICC flexibly captures complex…