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Related papers: Spatial Clustering of Galaxies in Large Datasets

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The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past merger events. While the majority of galaxies in the local…

Astrophysics of Galaxies · Physics 2023-02-23 Clár-Bríd Tohill , Steven Bamford , Christopher Conselice

We consider cosmological applications of galaxy number density correlations to be inferred from future deep and wide multi-band optical surveys. We mostly focus on very large scales as a probe of possible features in the primordial power…

Astrophysics · Physics 2009-11-13 Hu Zhan , Lloyd Knox , J. Anthony Tyson , Vera Margoniner

We propose an algorithm for clustering high dimensional data. If $P$ features for $N$ objects are represented in an $N\times P$ matrix ${\bf X}$, where $N\ll P$, the method is based on exploiting the cluster-dependent structure of the…

Machine Learning · Statistics 2018-11-05 Shahina Rahman , Valen E. Johnson

We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown. We propose a simple and low-complexity…

Information Theory · Computer Science 2013-03-18 Reinhard Heckel , Helmut Bölcskei

These lectures cover various aspects of the statistical description of cosmological density fields. Observationally, this consists of the point process defined by galaxies, and the challenge is to relate this to the continuous density field…

Astrophysics · Physics 2007-05-23 J. A. Peacock

Some years ago we proposed a new approach to the analysis of galaxy and cluster correlations based on the concepts and methods of modern statistical Physics. This led to the surprising result that galaxy correlations are fractal and not…

Astrophysics · Physics 2017-05-31 F. Sylos Labini , M. Montuori , L. Pietronero

Clustering can be defined as the process of assembling objects into a number of groups whose elements are similar to each other in some manner. As a technique that is used in many domains, such as face clustering, plant categorization,…

Machine Learning · Computer Science 2022-04-05 Mehmet F. Demirel , Enrico Au-Yeung

Object cross-identification in multiple observations is often complicated by the uncertainties in their astrometric calibration. Due to the lack of standard reference objects, an image with a small field of view can have significantly…

Instrumentation and Methods for Astrophysics · Physics 2015-06-05 Tamas Budavari , Stephen H. Lubow

Clustering aims to group unlabeled objects based on similarity inherent among them into clusters. It is important for many tasks such as anomaly detection, database sharding, record linkage, and others. Some clustering methods are taken as…

Databases · Computer Science 2024-12-02 Binbin Gu , Saeed Kargar , Faisal Nawab

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…

Cosmology and Nongalactic Astrophysics · Physics 2017-07-26 Elisabeth Krause , Tim Eifler

We describe the first results from two observational projects aimed at measuring the amount and spatial distribution of dark matter in distant early-type galaxies (E/S0s) and clusters of galaxies. At the galaxy scale, the Lenses Structure…

Astrophysics · Physics 2007-05-23 T. Treu , L. V. E. Koopmans , D. J. Sand , G. P. Smith , R. S. Ellis

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…

Instrumentation and Methods for Astrophysics · Physics 2022-09-14 Dimitrios Kelesis , Spyros Basilakos , Vicky Papadopoulou Lesta , Dimitris Fotakis , Andreas Efstathiou

Multiple images, giant arcs, Einstein cross, fold, cusp, lip, caustics, critical lines, sources, mapping, time delay, arclets, weak shear, magnification bias, depletion, ellipticities, polarization, smearing, convergence, kernel, mass…

Astrophysics · Physics 2007-05-23 Jean-Paul Kneib

We propose a novel technique to refine the modelling of galaxy clusters mass distribution using gravitational lensing. The idea is to combine the strengths of both "parametric" and "non-parametric" methods to improve the quality of the fit.…

Cosmology and Nongalactic Astrophysics · Physics 2009-11-13 Eric Jullo , Jean-Paul Kneib

We explore three different methods based on weak lensing to extract cosmological constraints from the large-scale structure. In the first approach (method I), small-scale galaxy lensing measurements of their halo mass provide a constraint…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-05 Jaiyul Yoo , Uros Seljak

In this work we show how galaxy clusters can be used to discriminate among different cosmological models. We have used available X-ray & optical cluster data to constrain the cosmological parameters as well as the cluster scaling relations,…

Astrophysics · Physics 2009-10-31 J. M. Diego , E. Martinez-Gonzalez , J. L. Sanz , L. Cayon , J. Silk

[Abridged] Galaxy clusters are the most massive gravitationally-bound systems in the universe and are widely considered to be an effective cosmological probe. We propose the first Machine Learning method using galaxy cluster properties to…

While clusters of galaxies are considered one of the most important cosmological probes, the standard spherical modelling of the dark matter and the intracluster medium is only a rough approximation. Indeed, it is well established both…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-11 Marceau Limousin , Andrea Morandi , Mauro Sereno , Massimo Meneghetti , Stefano Ettori , Matthias Bartelmann , Tomas Verdugo

Astrophysics has become a domain extremely rich of scientific data. Data mining tools are needed for information extraction from such large datasets. This asks for an approach to data management emphasizing the efficiency and simplicity of…

Databases · Computer Science 2009-11-10 M. Frailis , A. De Angelis , V. Roberto