Related papers: Evaluating the feasibility of interpretable machin…
The physical properties of the intracluster medium (ICM) reflect signatures of the underlying gravitational potential, mergers and strong interactions with other halos and satellite galaxies, as well as galactic feedback from supernovae and…
Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to…
Representing single stellar populations, globular clusters (GCs) are relatively easy to model, thus providing powerful tools for studying the evolution of galaxies. This has been demonstrated for the blue compact galaxy ESO338-IG04. GC…
This paper is the first in a series in which we perform an extensive comparison of various galaxy-based cluster mass estimation techniques that utilise the positions, velocities and colours of galaxies. Our primary aim is to test the…
Building accurate and interpretable Machine Learning (ML) models for heterogeneous/mixed data is a long-standing challenge for algorithms designed for numeric data. This work focuses on developing numeric coding schemes for non-numeric…
Globular clusters (GCs) are the most ancient stellar systems in the Milky Way. Therefore, they play a key role in the understanding of the early chemical and dynamical evolution of our Galaxy. Around $40\%$ of them are placed within $\sim4$…
We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…
Empirically, the total number (or total mass) of globular clusters bound in a single galactic system correlates with the viral mass of the system. The form of this relation and its intrinsic scatter are potentially valuable constraints on…
Interpretability can be critical for the safe and responsible use of machine learning models in high-stakes applications. So far, evolutionary computation (EC), in particular in the form of genetic programming (GP), represents a key enabler…
We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning $0 \leq z \leq 2$ from \textsc{The Three Hundred} project, a suite of…
Globular clusters are compact, gravitationally bound systems of up to a million stars. The GCs in the Milky Way contain some of the oldest stars known, and provide important clues to the early formation and continuing evolution of our…
We present several machine learning (ML) models developed to efficiently separate stars formed in-situ in Milky Way-type galaxies from those that were formed externally and later accreted. These models, which include examples from…
An analysis of globular cluster systems associated with galaxies included in the Virgo and Fornax HST Advanced Camera Surveys reveals distinct (g-z) colour modulation patterns. These features appear on composite samples of globular clusters…
Globular clusters play a role in many areas of astrophysics, ranging from stellar physics to cosmology. New ground-based optical surveys complemented by observations from space-based telescopes with unprecedented near-infrared capabilities…
We have investigated the global properties of the globular cluster (GC) systems of three early-type galaxies: the Virgo elliptical NGC4406, the field elliptical NGC3379, and the field S0 galaxy NGC4594. These galaxies were observed as part…
We investigate the ability of machine learning to infer the virial mass ($M_{\rm vir}$) and the scale radius ($r_{\rm s}$) of galaxy clusters from their observables. Using the Uchuu--UniverseMachine galaxy catalog at $z=0.093$, we generate…
Detailed understanding of the formation and evolution of globular clusters (GCs) has been recently advanced through a combination of numerical simulations and analytical models. We employ a state-of-the-art model to create a comprehensive…
We present a dataset built for machine learning applications consisting of galaxy photometry, images, spectroscopic redshifts, and structural properties. This dataset comprises 286,401 galaxy images and photometry from the Hyper-Suprime-Cam…
Astronomers have constructed models of globular clusters for over 100 years. These models mainly fall into two categories: (i) static models, such as King's model and its variants, and (ii) evolutionary models. Most attention has been given…