54,611 research outputs found
Multiple Uncertainties in Time-Variant Cosmological Particle Data
Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables
Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts
Getting the overall picture of how a large number of ego-networks evolve is a
common yet challenging task. Existing techniques often require analysts to
inspect the evolution patterns of ego-networks one after another. In this
study, we explore an approach that allows analysts to interactively create
spatial layouts in which each dot is a dynamic ego-network. These spatial
layouts provide overviews of the evolution patterns of ego-networks, thereby
revealing different global patterns such as trends, clusters and outliers in
evolution patterns. To let analysts interactively construct interpretable
spatial layouts, we propose a data transformation pipeline, with which analysts
can adjust the spatial layouts and convert dynamic egonetworks into event
sequences to aid interpretations of the spatial positions. Based on this
transformation pipeline, we developed Segue, a visual analysis system that
supports thorough exploration of the evolution patterns of ego-networks.
Through two usage scenarios, we demonstrate how analysts can gain insights into
the overall evolution patterns of a large collection of ego-networks by
interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2018
Calibration of semi-analytic models of galaxy formation using Particle Swarm Optimization
We present a fast and accurate method to select an optimal set of parameters
in semi-analytic models of galaxy formation and evolution (SAMs). Our approach
compares the results of a model against a set of observables applying a
stochastic technique called Particle Swarm Optimization (PSO), a self-learning
algorithm for localizing regions of maximum likelihood in multidimensional
spaces that outperforms traditional sampling methods in terms of computational
cost. We apply the PSO technique to the SAG semi-analytic model combined with
merger trees extracted from a standard CDM N-body simulation. The
calibration is performed using a combination of observed galaxy properties as
constraints, including the local stellar mass function and the black hole to
bulge mass relation. We test the ability of the PSO algorithm to find the best
set of free parameters of the model by comparing the results with those
obtained using a MCMC exploration. Both methods find the same maximum
likelihood region, however the PSO method requires one order of magnitude less
evaluations. This new approach allows a fast estimation of the best-fitting
parameter set in multidimensional spaces, providing a practical tool to test
the consequences of including other astrophysical processes in SAMs.Comment: 11 pages, 4 figures, 1 table. Accepted for publication in ApJ.
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