Related papers: Machine Learning Etudes in Astrophysics: Selection…
Stacking cosmic microwave background (CMB) maps around known galaxy clusters and groups provides a powerful probe of the distribution of hot gas in these systems via the Sunyaev-Zel'dovich (SZ) effect. A stacking analysis allows one to…
We present a new application of deep learning to reconstruct the cosmic microwave background (CMB) temperature maps from the images of microwave sky, and to use these reconstructed maps to estimate the masses of galaxy clusters. We use a…
We present the ROGER (Reconstructing Orbits of Galaxies in Extreme Regions) code, which uses three different machine learning techniques to classify galaxies in, and around, clusters, according to their projected phase-space position. We…
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…
Comparisons between observed and predicted strong lensing properties of galaxy clusters have been routinely used to claim either tension or consistency with $\Lambda$CDM cosmology. However, standard approaches to such cosmological tests are…
We apply simple optical and SZ cluster finders to mock galaxy catalogues and SZ flux maps created from dark matter halos in a (1 Gpc/h)^3 dark matter simulation, at redshifts 0.5 and 0.9. At each redshift, the two catalogues are then…
We present a very large high-resolution cosmological N-body simulation, the Millennium-XXL or MXXL, which uses 303 billion particles to represent the formation of dark matter structures throughout a 4.1Gpc box in a LambdaCDM cosmology. We…
We develop a novel method to explore the galaxy-halo connection using the galaxy imaging surveys by modeling the projected two-point correlation function measured from the galaxies with reasonable photometric redshift measurements. By…
We present mock catalogs created to support the interpretation of the CANDELS survey. We extract halos along past lightcones from the Bolshoi Planck dissipationless N-body simulations and populate these halos with galaxies using two…
When analyzing galaxy clustering in multi-band imaging surveys, there is a trade-off between selecting the largest galaxy samples (to minimize the shot noise) and selecting samples with the best photometric redshift (photo-z) precision,…
We present a study of the weak lensing inferred matter profiles $\Delta\Sigma(R)$ of 698 South Pole Telescope thermal Sunyaev-Zel'dovich effect selected and MCMF optically confirmed galaxy clusters in the redshift range $0.25 <z< 0.94$ that…
The growth of sky surveys and the large amount of stellar spectra in the current databases, has generated the necessity of developing new methods to estimate atmospheric parameters, a fundamental task on stellar research. In this work we…
The thermal Sunyaev-Zel'dovich effect is an important probe of clusters of galaxies, and has the attractive property of being proportional to the thermal energy content of the intracluster medium. With the assistance of X-ray data, the…
Image clustering is a very useful technique that is widely applied to various areas, including remote sensing. Recently, visual representations by self-supervised learning have greatly improved the performance of image clustering. To…
Sunyaev-Zel'dovich (SZ) surveys are promising probes of cosmology - in particular for Dark Energy (DE) -, given their ability to find distant clusters and provide estimates for their mass. However, current SZ catalogs contain tens to…
The two-point correlation function of the galaxy distribution is a key cosmological observable that allows us to constrain the dynamical and geometrical state of our Universe. To measure the correlation function we need to know both the…
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine…
In order to study the status and the possible evolution of clusters of galaxies at intermediate redshifts (z ~ 0.1 - 0.3), as well as their spatial correlation and relationship with the local environment, we built a sample of candidate…
We apply two machine learning methods, a CNN deep-leaning model and a gradient-boosting decision tree, to estimate individual cluster optical depths from observed properties derived from multiple complementary datasets. The models are…
The Canadian Cluster Comparison Project is a comprehensive multi-wavelength survey targeting 50 massive X-ray selected clusters of galaxies to examine baryonic tracers of cluster mass and to probe the cluster-to-cluster variation in the…