相关论文: Virtual instrumentation to study galaxies

JINGLE is a new JCMT legacy survey designed to systematically study the cold interstellar medium of galaxies in the local Universe. As part of the survey we perform 850um continuum measurements with SCUBA-2 for a representative sample of…

Halo is one of the most important basic elements in cosmology simulation, which merges from small clumps to ever larger objects. The processes of the birth and merging of the halos play a fundamental role in studying the evolution of large…

We have developed a simple yet surprisingly accurate analytic scheme for tracking the dynamical evolution of substructure within larger dark halos. The scheme incorporates the effects of dynamical friction, tidal mass loss and tidal heating…

I present an overview of our ongoing project aimed at building a new generation of velocity dispersion profiles ad rotation curves for a representative sample of Galactic globular clusters, from the the radial velocity of hundreds…

Visualization techniques are well developed for many problem domains, but these systems break down for datasets which are very large or multidimensional. Techniques for data which is discrete rather than continuous are also less well…

LISA is a space-based mHz gravitational-wave observatory, with a planned launch in 2034. It is expected to be the first detector of its kind, and will present unique challenges in instrumentation and data analysis. An accurate preflight…

Recent theoretical work and targeted observational studies suggest that filaments are sites of galaxy preprocessing. The aim of the WISESize project is to directly probe galaxies over the full range of environments to quantify and…

We introduce an open-source system called SIGMA (short for "Situated Interactive Guidance, Monitoring, and Assistance") as a platform for conducting research on task-assistive agents in mixed-reality scenarios. The system leverages the…

We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MULTIMODAL UNIVERSE contains hundreds of millions of…

Since 2003 the Planetary Virtual Observatory and Laboratory (PVOL) has been storing and serving publicly through its web site a large database of amateur observations of the Giant Planets (Hueso et al., 2010a). These images are used for…

Gravitational lensing is a fundamental tool for cosmology. A recent instrument which will provide more information for models of these objects is ALMA. Our goal is to select lens candidates to observe with ALMA and then model them using…

We present GIGANTES, the most extensive and realistic void catalog suite ever released -- containing over 1 billion cosmic voids covering a volume larger than the observable Universe, more than 20 TB of data, and created by running the void…

We present two different projects. The first one is related to the development of a semi-analytic model to follow the formation, the evolution and chemical enrichment of cluster galaxies in a hierarchical dark matter model. The second…

This paper introduces a novel toolbox named BIDEAL for the generation of biclusters, their analysis, visualization, and validation. The objective is to facilitate researchers to use forefront biclustering algorithms embedded on a single…

Personalized medicine remains a major challenge for scientists. The rapid growth of Machine learning and Deep learning has made them a feasible al- ternative for predicting the most appropriate therapy for individual patients. However, the…

Simulated galaxy distributions are suitable for developing filament detection algorithms. However, samples of observed galaxies, being of limited size, cause difficulties that lead to a discontinuous distribution of filaments. We created a…

We introduce a new suite of 1,024 cosmological and hydrodynamical zoom-in simulations of Milky Way-mass halos, run with Cold Dark Matter, as part of the DREAMS Project. Each simulation in the suite has a unique set of initial conditions and…

Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they…

We describe Ganalyzer, a model-based tool that can automatically analyze and classify galaxy images. Ganalyzer works by separating the galaxy pixels from the background pixels, finding the center and radius of the galaxy, generating the…

Manifold Learning is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus manifold learning algorithms are, at least in theory, most applicable to high-dimensional data and sample sizes to…