Related papers: COSMIC: A Galaxy Cluster Finding Algorithm Using M…
We explore strategies to extract cosmological constraints from a joint analysis of cosmic shear, galaxy-galaxy lensing, galaxy clustering, cluster number counts and cluster weak lensing. We utilize the CosmoLike software to simulate results…
Context: We present the first Cosmological Parameter inferences from eROSITA X-ray observations of galaxy clusters using a Machine Learning algorithm. Methods: We train a Random Forest using mock catalogs of clusters from Magneticum…
We present AMICO (Adaptive Matched Identifier of Clustered Objects), a new algorithm for the detection of galaxy clusters in photometric surveys. AMICO is based on the Optimal Filtering technique, which allows to maximise the…
Clusters of galaxies are important laboratories for understanding both galaxy evolution and constraining cosmological quantities. Any analysis of clusters, however, is best done when one can reliably determine which galaxies are members of…
We introduce cosmocnc, a Python package for computing the number count likelihood of galaxy cluster catalogues in a fast, flexible and accurate way. cosmocnc offers three types of likelihoods: an unbinned, a binned, and an extreme value…
We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder…
Galaxy groups with $M_{tot} \lesssim 10^{14}$ $M_\odot$ and up to a few tens of members are the most common galaxy environment, marking the transition between field and massive clusters. Identifying groups plays a crucial role in…
In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…
Datasets with tens of millions of galaxies present new challenges for the analysis of spatial clustering. We have built a framework that integrates a database of object catalogs, tools for creating masks of bad regions, and a fast (NlogN)…
We use a sample of galaxies from the Two Micron All Sky Survey (2MASS) Extended Source Catalog to refine a matched filter method of finding galaxy clusters that takes into account each galaxy's position, magnitude, and redshift if…
We present preliminary results of a simple peak finding algorithm applied to catalogues of galaxies, extracted from the Second Palomar Sky Survey in the framework of the CRoNaRio project. All previously known Abell and Zwicky clusters in a…
Measurements of galaxy cluster abundances, clustering properties, and mass to- light ratios in current and future surveys can provide important cosmological constraints. Digital wide-field imaging surveys, the recently-demonstrated fidelity…
In this paper we explore the use of spatial clustering algorithms as a new computational approach for modeling the cosmic web. We demonstrate that such algorithms are efficient in terms of computing time needed. We explore three distinct…
In this work we present a new catalogue of Cosmic Filaments obtained from the latest Sloan Digital Sky Survey (SDSS) public data. In order to detect filaments, we implement a version of the Subspace-Constrained Mean-Shift algorithm, boosted…
Reliable extraction of cosmological information from clustering measurements of galaxy surveys requires estimation of the error covariance matrices of observables. The accuracy of covariance matrices is limited by our ability to generate…
In the last years, Astroinformatics has become a well defined paradigm for many fields of Astronomy. In this work we demonstrate the potential of a multidisciplinary approach to identify globular clusters (GCs) in the Fornax cluster of…
Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…
In order to enlarge publicly available optical cluster catalogs, in particular at high redshift, we have performed a systematic search for clusters of galaxies in the CFHTLS. We used the Le Phare photometric redshifts for the galaxies…
We outline here the next generation of cluster-finding algorithms. We show how advances in Computer Science and Statistics have helped develop robust, fast algorithms for finding clusters of galaxies in large multi-dimensional astronomical…
Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…