253 research outputs found
CARMA Large Area Star Formation Survey: Project Overview with Analysis of Dense Gas Structure and Kinematics in Barnard 1
We present details of the CARMA Large Area Star Formation Survey (CLASSy),
while focusing on observations of Barnard 1. CLASSy is a CARMA Key Project that
spectrally imaged N2H+, HCO+, and HCN (J=1-0 transitions) across over 800
square arcminutes of the Perseus and Serpens Molecular Clouds. The observations
have angular resolution near 7" and spectral resolution near 0.16 km/s. We
imaged ~150 square arcminutes of Barnard 1, focusing on the main core, and the
B1 Ridge and clumps to its southwest. N2H+ shows the strongest emission, with
morphology similar to cool dust in the region, while HCO+ and HCN trace several
molecular outflows from a collection of protostars in the main core. We
identify a range of kinematic complexity, with N2H+ velocity dispersions
ranging from ~0.05-0.50 km/s across the field. Simultaneous continuum mapping
at 3 mm reveals six compact object detections, three of which are new
detections. A new non-binary dendrogram algorithm is used to analyze dense gas
structures in the N2H+ position-position-velocity (PPV) cube. The projected
sizes of dendrogram-identified structures range from about 0.01-0.34 pc.
Size-linewidth relations using those structures show that non-thermal
line-of-sight velocity dispersion varies weakly with projected size, while rms
variation in the centroid velocity rises steeply with projected size. Comparing
these relations, we propose that all dense gas structures in Barnard 1 have
comparable depths into the sky, around 0.1-0.2 pc; this suggests that
over-dense, parsec-scale regions within molecular clouds are better described
as flattened structures rather than spherical collections of gas. Science-ready
PPV cubes for Barnard 1 molecular emission are available for download.Comment: Accepted to The Astrophysical Journal (ApJ), 51 pages, 27 figures
(some with reduced resolution in this preprint); Project website is at
http://carma.astro.umd.edu/class
Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps
With the advent of large scale surveys the manual analysis and classification
of individual radio source morphologies is rendered impossible as existing
approaches do not scale. The analysis of complex morphological features in the
spatial domain is a particularly important task. Here we discuss the challenges
of transferring crowdsourced labels obtained from the Radio Galaxy Zoo project
and introduce a proper transfer mechanism via quantile random forest
regression. By using parallelized rotation and flipping invariant Kohonen-maps,
image cubes of Radio Galaxy Zoo selected galaxies formed from the FIRST radio
continuum and WISE infrared all sky surveys are first projected down to a
two-dimensional embedding in an unsupervised way. This embedding can be seen as
a discretised space of shapes with the coordinates reflecting morphological
features as expressed by the automatically derived prototypes. We find that
these prototypes have reconstructed physically meaningful processes across two
channel images at radio and infrared wavelengths in an unsupervised manner. In
the second step, images are compared with those prototypes to create a
heat-map, which is the morphological fingerprint of each object and the basis
for transferring the user generated labels. These heat-maps have reduced the
feature space by a factor of 248 and are able to be used as the basis for
subsequent ML methods. Using an ensemble of decision trees we achieve upwards
of 85.7% and 80.7% accuracy when predicting the number of components and peaks
in an image, respectively, using these heat-maps. We also question the
currently used discrete classification schema and introduce a continuous scale
that better reflects the uncertainty in transition between two classes, caused
by sensitivity and resolution limits
A Comparative Study of the Perceptual Sensitivity of Topological Visualizations to Feature Variations
Color maps are a commonly used visualization technique in which data are
mapped to optical properties, e.g., color or opacity. Color maps, however, do
not explicitly convey structures (e.g., positions and scale of features) within
data. Topology-based visualizations reveal and explicitly communicate
structures underlying data. Although we have a good understanding of what types
of features are captured by topological visualizations, our understanding of
people's perception of those features is not. This paper evaluates the
sensitivity of topology-based isocontour, Reeb graph, and persistence diagram
visualizations compared to a reference color map visualization for
synthetically generated scalar fields on 2-manifold triangular meshes embedded
in 3D. In particular, we built and ran a human-subject study that evaluated the
perception of data features characterized by Gaussian signals and measured how
effectively each visualization technique portrays variations of data features
arising from the position and amplitude variation of a mixture of Gaussians.
For positional feature variations, the results showed that only the Reeb graph
visualization had high sensitivity. For amplitude feature variations,
persistence diagrams and color maps demonstrated the highest sensitivity,
whereas isocontours showed only weak sensitivity. These results take an
important step toward understanding which topology-based tools are best for
various data and task scenarios and their effectiveness in conveying
topological variations as compared to conventional color mapping
Task-based Augmented Contour Trees with Fibonacci Heaps
This paper presents a new algorithm for the fast, shared memory, multi-core
computation of augmented contour trees on triangulations. In contrast to most
existing parallel algorithms our technique computes augmented trees, enabling
the full extent of contour tree based applications including data segmentation.
Our approach completely revisits the traditional, sequential contour tree
algorithm to re-formulate all the steps of the computation as a set of
independent local tasks. This includes a new computation procedure based on
Fibonacci heaps for the join and split trees, two intermediate data structures
used to compute the contour tree, whose constructions are efficiently carried
out concurrently thanks to the dynamic scheduling of task parallelism. We also
introduce a new parallel algorithm for the combination of these two trees into
the output global contour tree. Overall, this results in superior time
performance in practice, both in sequential and in parallel thanks to the
OpenMP task runtime. We report performance numbers that compare our approach to
reference sequential and multi-threaded implementations for the computation of
augmented merge and contour trees. These experiments demonstrate the run-time
efficiency of our approach and its scalability on common workstations. We
demonstrate the utility of our approach in data segmentation applications
Interchanging Interactive 3-d Graphics for Astronomy
We demonstrate how interactive, three-dimensional (3-d) scientific
visualizations can be efficiently interchanged between a variety of mediums.
Through the use of an appropriate interchange format, and a unified interaction
interface, we minimize the effort to produce visualizations appropriate for
undertaking knowledge discovery at the astronomer's desktop, as part of
conference presentations, in digital publications or as Web content. We use
examples from cosmological visualization to address some of the issues of
interchange, and to describe our approach to adapting S2PLOT desktop
visualizations to the Web.
Supporting demonstrations are available at
http://astronomy.swin.edu.au/s2plot/interchange/Comment: 10 pages, 7 figures, submitted to Publications of the Astronomical
Society of Australia. v2. Revised title, revised figure 1, fixed typos, minor
additions to future work sectio
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