Related papers: DeepSZ: Identification of Sunyaev-Zel'dovich Galax…

We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky. Effectively, this is a novel approach to determining the scaling relation between a cluster's Sunyaev-Zel'dovich…

The Planck satellite was launched in 2009 by the European Space Agency to study the properties of the cosmic microwave background (CMB). An expected result of the Planck data analysis is the distinction of the various contaminants of the…

The problem of detecting Sunyaev-Zel'dovich (SZ) clusters in multifrequency CMB observations is investigated using a number of filtering techniques. A multifilter approach is introduced, which optimizes the detection of SZ clusters on…

The Planck collaboration has extensively used the six Planck HFI frequency maps to detect the Sunyaev-Zel'dovich (SZ) effect with dedicated methods, e.g., by applying (i) component separation to construct a full sky map of the y parameter…

We evaluate the construction methodology of an all-sky catalogue of galaxy clusters detected through the Sunyaev-Zel'dovich (SZ) effect. We perform an extensive comparison of twelve algorithms applied to the same detailed simulations of the…

The hot ionized gas of the intra-cluster medium emits thermal radiation in the X-ray band and also distorts the cosmic microwave radiation through the Sunyaev-Zel'dovich (SZ) effect. Combining these two complementary sources of information…

We present a new method for the reconstruction of Sunyaev-Zel'dovich (SZ) galaxy clusters in future SZ-survey experiments using multiband bolometer cameras such as Olimpo, APEX, or Planck. Our goal is to optimise SZ-Cluster extraction from…

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…

Thanks to advances in detector technology and observing techniques, true Sunyaev-Zel'dovich (SZ) surveys will soon become a reality. This opens up a new window into the Universe, in many ways analogous to the X-ray band and inherently…

High-frequency, high-resolution imaging of the Sunyaev-Zel'dovich (SZ) effect is an important technique to study the complex structures of the atmospheres of merging galaxy clusters. Such observations are sensitive to the details of the…

We have created mock Sunyaev-Zel'dovich effect (SZE) surveys of galaxy clusters using high resolution N-body simulations. To the pure surveys we add `noise' contributions appropriate to instrument and primary CMB anisotropies. Applying…

We develop and test a new statistical method to measure the kinematic Sunyaev-Zel'dovich (kSZ) effect. A sample of independently detected clusters is combined with the cosmic flow field predicted from a galaxy redshift survey in order to…

The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a…

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…

The Sunyaev--Zel'dovich (SZ) effect is a global distortion of the Cosmic Microwave Background (CMB) spectrum as a result of its interaction with a hot electron plasma in the intracluster medium of large structures gravitationally viralized…

We present a new method for component separation aimed to extract Sunyaev-Zeldovich (SZ) galaxy clusters from multifrequency maps of Cosmic Microwave Background (CMB) experiments. This method is designed to recover non-Gaussian, spatially…

We present a machine-learning approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep machine learning tool commonly used in image recognition tasks. The CNN is trained…

We describe the observations and resultant galaxy cluster catalog from the 2770 deg$^2$ SPTpol Extended Cluster Survey (SPT-ECS). Clusters are identified via the Sunyaev-Zel'dovich (SZ) effect, and confirmed with a combination of archival…

We present a comparison of three cluster finding algorithms from imaging data using Monte Carlo simulations of clusters embedded in a 25 deg^2 region of Sloan Digital Sky Survey (SDSS) imaging data: the Matched Filter (MF; Postman et al.…