Related papers: DeepSZ: Identification of Sunyaev-Zel'dovich Galax…
The Canada-France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ~5000 square degrees of the sky, representing a first-rate opportunity to identify recently-merged galaxies. Due to the large number of…
An extensive sample of galaxy clusters will be available in the coming years, detected through their Sunyaev-Zeldovich effect (SZE). We use a semi-analytic model to study the scientific yield of combining SZE data with X-ray and optical…
We present a catalog of 4195 optically confirmed Sunyaev-Zel'dovich (SZ) selected galaxy clusters detected with signal-to-noise > 4 in 13,211 deg$^2$ of sky surveyed by the Atacama Cosmology Telescope (ACT). Cluster candidates were selected…
The measurement of angular diameter distance to galaxy clusters, through combined Sunyaev-Zel'dovich (SZ) effect data with X-ray emission observations, is now a well-known probe of cosmology. Using a combination of SZ data and a map of the…
We present a new method for component separation from multifrequency maps aimed to extract Sunyaev-Zeldovich (SZ) galaxy clusters from Cosmic Microwave Background (CMB) experiments. This method is best suited to recover non--Gaussian,…
Galaxy clusters are the most massive gravitationally bound systems consisting of dark matter, hot baryonic gas and stars. They play an important role in observational cosmology and galaxy evolution studies. We develop a deep learning model…
We present the first systematic follow-up of Planck Sunyaev-Zeldovich effect (SZE) selected candidates down to signal-to-noise (S/N) of 3 over the 5000 deg$^2$ covered by the Dark Energy Survey. Using the MCMF cluster confirmation…
We present a quantitative study of the X-ray morphology of galaxy clusters, as a function of their detection method and redshift. We analyze two separate samples of galaxy clusters: a sample of 36 clusters at 0.35 < z < 0.9 selected in the…
The fluctuations in the Cosmic Microwave Background (CMB) intensity due to the Sunyaev-Zeldovich (SZ) effect are the sum of a thermal and a kinetic contribution. Separating the two components to measure the peculiar velocity of galaxy…
Context. Convolutional neural networks (CNNs) have been proven to perform fast classification and detection on natural images and have potential to infer astrophysical parameters on the exponentially increasing amount of sky survey imaging…
We present the first lensing mass measurements of Sunyaev-Zel'dovich (SZ) selected clusters. Using optical imaging from the Southern Cosmology Survey (SCS), we present weak lensing masses for three clusters selected by their SZ emission in…
The Sunyaev-Zeldovich (SZ) effect is a promising tool for detecting the presence of hot gas out to the galaxy cluster peripheries. We developed a spectral imaging algorithm dedicated to the SZ observations of nearby galaxy clusters with…
We perform the first joint analysis of galaxy clustering (GC) and the kinetic Sunyaev-Zel'dovich (kSZ) effect to simultaneously constrain cosmological and astrophysical parameters in this work, utilizing a combination of the Atacama…
We study the nature of cluster selection in Sunyaev-Zel'dovich (SZ) surveys, focusing on single frequency observations and using Monte Carlo simulations incorporating instrumental effects, primary cosmic microwave background (CMB)…
Deep learning methods have been successfully applied to remote sensing problems for several years. Among these methods, CNN based models have high accuracy in solving the land classification problem using satellite or aerial images.…
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 introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on convolutional neural network (CNN). Training datasets are generated with ray-tracing through…
We develop a machine learning algorithm to infer the 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich effect maps. We generate around 73,000 mock images along various lines of sight…
We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers. Moreover, we investigate the use of transfer learning in conjunction with CNNs, by retraining networks first trained…
Galaxy clusters and surrounding medium, can be studied using X-ray bremsstrahlung emission and Sunyaev Zel'dovich (SZ) effect. Both astrophysical probes, sample the same environment with different parameters dependance. The SZ effect is…