Related papers: Machine Learning Etudes in Astrophysics: Selection…
Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still…
We show how to place constraints on cluster physics by stacking the weak lensing signals from multiple clusters found through the Sunyaev-Zeldovich (SZ) effect. For a survey that covers about 200 sq. deg. both in SZ and weak lensing…
As the quality of the available galaxy cluster data improves, the models fitted to these data might be expected to become increasingly complex. Here we present the Bayesian approach to the problem of cluster data modelling: starting from…
The thermal Sunyaev-Zeldovich (tSZ) effect is associated with galaxy clusters - extremely large and dense structures tracing the dark matter with a higher bias than isolated galaxies. We propose to use the tSZ data to separate galaxies from…
We propose an improved methodology to constrain spatial variations of the fine structure constant using clusters of galaxies. We use the {\it Planck} 2013 data to measure the thermal Sunyaev-Zeldovich effect at the location of 618 X-ray…
We use mock Sunyaev-Zel'dovich effect (SZE) maps to investigate how well the Planck mission might find and characterize clusters of galaxies. We discuss different combinations of frequency maps and different methods for identifying cluster…
In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and…
Clusters of galaxies can be identified from peaks in weak lensing aperture mass maps constructed from weak lensing shear catalogs. Such purely gravitational cluster selection considerably differs from traditional cluster selections based on…
The correlation function of galaxy clusters has often been used as a test of cosmological models. A number of assumptions are implicit in the comparison of theoretical expectations to data. Here we use an ensemble of ten large N-body…
We present the X-ray source detection procedure that we have developed for the purpose of assembling and characterizing controlled samples of cluster of galaxies for the XMM Large Scale Structure Survey. We describe how we model the…
Multi-frequency matched filters (MMFs) are routinely used to detect galaxy clusters from CMB data through the thermal Sunyaev-Zeldovich (tSZ) effect, leading to cluster catalogues that can be used for cosmological inference. In order to be…
Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. Yet modeling the selection function of such samples is complicated by non-cluster members projected along the line of…
We develop a machine learning algorithm that generates high-resolution thermal Sunyaev-Zeldovich (SZ) maps of novel galaxy clusters given only halo mass and mass accretion rate. The algorithm uses a conditional variational autoencoder…
We present an automated approach to detect and extract information from the astronomical datasets on the shapes of such objects as galaxies, star clusters and, especially, elongated ones such as the gravitational lenses. First, the…
Galaxy clusters are one of the important cosmological probes to test the consistency of the observable structure and evolution of our Universe with the predictions of specific cosmological models. We use results from our analysis of the…
The eROSITA X-ray telescope, launched in 2019, is predicted to observe roughly 100,000 galaxy clusters. Follow-up observations of these clusters from Chandra, for example, will be needed to resolve outstanding questions about galaxy cluster…
We use simulated maps to investigate the ability of high resolution, low noise surveys of the CMB to create catalogues of Clusters of galaxies by detecting the characteristic signature imprinted by the Sunyaev Zeldovich effect. We compute…
Combinations of galaxy surveys and cosmic microwave background secondaries, such as the thermal Sunyaev Zeldovich (tSZ) effect, are increasingly being used to jointly constrain cosmology and astrophysical properties of the gas within and…
Classification is a popular task in the field of Machine Learning (ML) and Artificial Intelligence (AI), and it happens when outputs are categorical variables. There are a wide variety of models that attempts to draw some conclusions from…
Machine learning is a powerful technique, becoming increasingly popular in astrophysics. In this paper, we apply machine learning to more than a thousand globular cluster (GC) models simulated as part of the 'MOCCA-Survey Database I'…