Related papers: What to expect from dynamical modelling of cluster…
We present a machine learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2,041 clusters from the Magneticum simulations. We train a random forest regressor, an ensemble…
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…
We study the connection between morphology and dynamical state of the simulated galaxy clusters in $z\in[0,1.031]$ from THE THREE HUNDRED Project. We quantify cluster dynamical state using a combination of dynamical indicators from…
Galaxy clusters provide a unique environment to study galaxy evolution. The role of cluster dynamical states in shaping the physical and morphological properties of member galaxies remains an open question. We aim to assess the impact of…
Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the…
We use the Millennium Simulation series to study how the dynamical state of dark matter halos affects the relation between mass and concentration. We find that a large fraction of massive systems are identified when they are substantially…
Accurate estimation of photometric redshifts (photo-$z$) is crucial in studies of both galaxy evolution and cosmology using current and future large sky surveys. In this study, we employ Random Forest (RF), a machine learning algorithm, to…
Joint lensing and dynamical mass profile determinations of galaxy clusters are an excellent tool to constrain modification of gravity at cosmological scales. However, search for tiny departures from General Relativity calls for an accurate…
We present a new approach for quantifying the abundance of galaxy clusters and constraining cosmological parameters using dynamical measurements. In the standard method, galaxy line-of-sight (LOS) velocities, $v$, or velocity dispersions…
Halo bias links the statistical properties of the spatial distribution of dark matter halos to those of the underlying dark matter field, providing insights into clustering properties in both general relativity (GR) and modified-gravity…
Using a suite of cosmology simulations of a sample of $> 120$ galaxy clusters with $\log(M_{DM, vir}) \le 14.5$. We compare clusters that form in purely dark matter run and their counterparts in hydro runs and investigate 4 independent…
While the influence of galaxy clusters on galaxy evolution is relatively well-understood, the impact of the dynamical states of these clusters is less clear. This paper series explores how the dynamical state of galaxy clusters affects…
Context:Halo formation time, which quantifies the mass assembly history of dark-matter halos, directly impacts galaxy properties and evolution. Although not directly observable, it can be inferred through proxies like star formation history…
The dynamical state of galaxy clusters describes if clusters are relaxed dynamically or in a merging process of subclusters. By using archival images from the Chandra X-ray Observatory, we derive a set of parameters to describe the…
Using a novel machine learning method, we investigate the buildup of galaxy properties in different simulations, and in various environments within a single simulation. The aim of this work is to show the power of this approach at…
Using hydrodynamical simulations, we study how well the underlying gravitational potential of a galaxy cluster can be modelled dynamically with different types of tracers. In order to segregate different systematics and the effects of…
Dynamical state of galaxy clusters is closely related to their observational properties in X-ray, optical and radio wavelengths. We develop a method to diagnose the substructure and dynamical state of galaxy clusters by using photometric…
Using the galaxy clusters from The Three Hundred Project, we define a new parameter: $\lambda_{DS}$ to describe the dynamical state of clusters, which assumes a double-Gaussian distribution in logarithm scale for our mass-complete cluster…
We investigate the clustering of galaxy groups and clusters in the SDSS using the Berlind et al. (2006) group sample, which is designed to identify galaxy systems that each occupy a single dark matter halo. We estimate group masses from…
Feature selection in machine learning is subject to the intrinsic randomness of the feature selection algorithms (for example, random permutations during MDA). Stability of selected features with respect to such randomness is essential to…