Related papers: Using X-Ray Morphological Parameters to Strengthen…
The infall regions of galaxy clusters represent the largest gravitationally bound structures in a $\Lambda$CDM universe. Measuring cluster mass profiles into the infall regions provides an estimate of the ultimate mass of these haloes. We…
Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide…
We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that…
A precise determination of the mass function is an important tool to verify cosmological predictions of the $\Lambda$CDM model and to infer more precisely the better model describing the evolution of the Universe. Galaxy clusters have been…
In the era of large surveys, yielding thousands of galaxy clusters, efficient mass proxies at all scales are necessary in order to fully utilize clusters as cosmological probes. At the cores of strong lensing clusters, the Einstein radius…
We present a novel quantitative scheme of cluster classification based on the morphological properties that are manifested in X-ray images. We use a conventional radial surface brightness concentration parameter (c_{SB}) as defined…
The spatial extent of the environment's impact on galaxies marks a transitional region between cluster and field galaxies. We present a data-driven method to identify this region in galaxy clusters with masses $M_{200\rm ,mean}>10^{13}…
High-resolution N-body simulations are used to investigate systematic trends in the mass profiles and total masses of clusters as derived from 3 simple estimators: (1) the weak gravitational lensing shear field under the assumption of an…
The eROSITA X-ray telescope on board the Spectrum-Roentgen-Gamma (SRG) mission will measure the position and properties of about 100,000 clusters of galaxies and 3 million active galactic nuclei over the full sky. To study the statistical…
We test the reliability of mass determination in clusters of galaxies by X-ray observations. The true mass in cluster models is compared to the mass derived by the X-ray emission and X-ray temperature of a model assuming hydrostatic…
Identifying merging galaxies is an important - but difficult - step in galaxy evolution studies. We present random forest classifications of galaxy mergers from simulated JWST images based on various standard morphological parameters. We…
Understanding the cluster population of clusters of galaxies is of the utmost importance for using cluster samples in both astrophysical and cosmological studies. We present an in-depth analysis of the X-ray morphological parameters of the…
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…
We forecast the impact of weak lensing (WL) cluster mass calibration on the cosmological constraints from the X-ray selected galaxy cluster counts in the upcoming eROSITA survey. We employ a prototype cosmology pipeline to analyze mock…
We present the clustering of galaxy clusters as a useful addition to the common set of cosmological observables. The clustering of clusters probes the large-scale structure of the Universe, extending galaxy clustering analysis to the…
Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87's…
We compare X-ray and weak-lensing masses for four galaxy clusters that comprise the top-ranked shear-selected cluster system in the Deep Lens Survey. The weak-lensing observations of this system, which is associated with A781, are from the…
We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21,…
The nature of dark energy is imprinted in the large-scale structure of the Universe and thus in the mass and redshift distribution of galaxy clusters. The upcoming eROSITA mission will exploit this method of probing dark energy by detecting…
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…