Related papers: A Machine Learning Approach to Enhancing eROSITA O…
An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the…
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…
Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently.…
We present the first public release of our Bayesian inference tool, Bayes-X, for the analysis of X-ray observations of galaxy clusters. We illustrate the use of Bayes-X by analysing a set of four simulated clusters at z=0.2-0.9 as they…
Cosmological simulations are fundamental tools to study structure formation and the astrophysics of evolving structures, in particular clusters of galaxies. While hydrodynamical simulations cannot sample efficiently large volumes and…
Gradually, the $\Lambda$CDM model starts to be challenged by observational data. Upcoming cosmological surveys will increase the number of detected galaxy clusters by several orders of magnitude. Therefore, shortly, clusters will provide…
Clusters of galaxies can be used as powerful probes to study astrophysical processes on large scales, test theories of the growth of structure, and constrain cosmological models. The driving science goal of the SRG/eROSITA All-Sky Survey…
We present the first weak-lensing mass calibration and X-ray scaling relations of galaxy clusters and groups selected in the $eROSITA$ Final Equatorial Depth Survey (eFEDS) observed by Spectrum Roentgen Gamma/$eROSITA$ over a contiguous…
The precision of cosmological parameters derived from galaxy cluster surveys is limited by uncertainty in relating observable signals to cluster mass. We demonstrate that a small mass-calibration follow-up program can significantly reduce…
In recent years, machine learning (ML) algorithms have been successfully employed in Astronomy for analyzing and interpreting the data collected from various surveys. The need for new robust and efficient data analysis tools in Astronomy is…
Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…
Sensitive, wide-area X-ray surveys which would be possible with the WFXT will detect huge samples of virialized objects spanning the mass range from sub-groups to the most massive clusters, and extending in redshift to beyond z=2. These…
We present a deep X-ray selected sample of galaxy clusters which has been created from a serendipitous search in ROSAT-PSPC deep pointed observations at high galactic latitude. This survey, hereafter known as the ROSAT Deep Cluster Survey…
In searching for continuous gravitational waves over very many ($\approx 10^{17}$) templates , clustering is a powerful tool which increases the search sensitivity by identifying and bundling together candidates that are due to the same…
We test the predictions of Emergent Gravity using matter densities of relaxed, massive clusters of galaxies using observations from optical and X-ray wavebands. We improve upon previous work in this area by including the baryon mass…
Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool for the detection and quantification of these galaxy-scale…
We discuss an algorithm whereby the massive galaxy clusters detected in the SRG/eROSITA all-sky survey are identified and their photometric redshifts are estimated. For this purpose, we use photometric redshift estimates for galaxies and…
We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…
Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to…
Galaxy clusters are one of the most powerful probes to study extensions of General Relativity and the Standard Cosmological Model. Upcoming surveys like the Vera Rubin Observatory's Legacy Survey of Space and Time are expected to…