Related papers: Machine Learning to identify ICL and BCG in simula…
Context. The intracluster light (ICL) comprises stars that are not bound to individual galaxies within a galaxy cluster, and it provides insights into the cluster mass distribution, evolutionary history, and dynamical state. Aims. We study…
In-Context Learning (ICL) empowers Large Language Models (LLMs) with the ability to learn from a few examples provided in the prompt, enabling downstream generalization without the requirement for gradient updates. Despite encouragingly…
In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only…
Clustering is one of the most fundamental tasks in machine learning. Recently, deep clustering has become a major trend in clustering techniques. Representation learning often plays an important role in the effectiveness of deep clustering,…
In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach to tackle these problems. Many state-of-the-art methods find…
We investigate the intra-cluster light (ICL) in the 6 Hubble Frontier Field clusters at $0.3<z<0.6$. We employ a new method, which is free from any functional form of the ICL profile, and exploit the unprecedented depth of this Hubble Space…
The evolutionary classification of molecular clumps, crucial for understanding star formation, is commonly based on human-assigned categories derived from infrared (IR) emission and well-established morphological criteria. However, due to…
The alignment between brightest cluster galaxies (BCGs) and host clusters can reveal the mystery of formation and evolution for galaxy clusters. We measure cluster orientations in optical based on the projected distribution of member…
The bright central galaxies (BCGs) dominate the inner portion of the diffuse cluster light, but it is still unclear where the intracluster light (ICL) takes over. To investigate the BCG-ICL transition, we stack the images of ${\sim}3000$…
We train three convolutional neural networks (CNNs) to classify galaxies with Galaxy Zoo 2 dataset and extract the activations from the last fully connected layer or the last average pooling layer of CNNs to study the high-dimensional…
Cosmological (LCDM) TreeSPH simulations of galaxy clusters have been performed, invoking star-formation, chemical evolution with non-instantaneous recycling, metal dependent radiative cooling, strong star-burst and/or AGN driven…
As machine learning models are increasingly deployed in sensitive application areas, the demand for interpretable and trustworthy decision-making has increased. Random Forests (RF), despite their widespread use and strong performance on…
Recent hydrodynamic cosmological simulations cover volumes up to Gpc^3 and resolve halos across a wide range of masses and environments, from massive galaxy clusters down to normal galaxies, while following a large variety of physical…
We propose a random forest (RF) machine learning approach to determine the accreted stellar mass fractions ($f_\mathrm{acc}$) of central galaxies, based on various dark matter halo and galaxy features. The RF is trained and tested using…
Despite significant advances in clustering methods in recent years, the outcome of clustering of a natural image dataset is still unsatisfactory due to two important drawbacks. Firstly, clustering of images needs a good feature…
Our current knowledge of the thermodynamic properties of galaxy clusters comes primarily from detailed studies of clusters selected by their minority components: hot baryons. Most of these studies select the clusters using the component…
Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Therefore, identifying such systems allows to…
We analyzed the contribution of the intracluster light (ICL) to the total luminosity of two massive galaxy clusters observed by the Hubble Space Telescope within the Frontier Fields program, Abell 370 (z ~ 0.375) and Abell S1063 (z ~…
We present a set-based machine learning framework that infers posterior distributions of galaxy cluster masses from projected galaxy dynamics. Our model combines Deep Sets and conditional normalizing flows to incorporate both positional and…
In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…