SlicerAstro: an interactive 3D visual analytics tool for HI data
Davide Punzo*,
Thijs van der Hulst,
Jean-Christophe Fillion-Robin.
*D.Punzo@astro.rug.nl, punzodavide@hotmail.it
Image credit: T. Oosterloo
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Each data-cube contains ~100 HI sources
An Apertif
data-cube:
~ 1011 voxels
most (99%) will be dominated by noise
Data: Ramatsoku et al., 2016.
Apertif (Verheijen et al., 2009, in Panoramic Radio Astronomy, PoS )
will detect HI in hundreds of thousands of galaxies in the northern sky.
Data rate 10 cubes/week. We will enter in the Big data domain for two reasons:
WALLABY (Southern sky; Johnston at al., 2008)
will have similar characteristics
8deg2
Subcubes around sources will typically contain~ 105 voxels
Galaxies in the Ursa Major cluster
Data: Busekool and Verheijen
extra-planar gas tidal filaments ram-pressure tails
3D interactive
visualization techniques
can help finding such features
Image credit: M. Verheijen
Our aim
3D interactive
visualization
of HI datasets
Filtering and 3D
3D selection
Analysis, modeling
in full 3D
SlicerAstro
Downloading SlicerAstro
Well documented:
High level of modularity
Open Source
CPU/GPU Volume Rendering
Multi-platform (Qt5 and CMake)
Long-term maintainability
Synergies between astronomical and medical visualization
www.slicer.org
See also Punzo et al., 2015,
Astronomy and Computing, 12, 86.
3DSlicer
Data:
M. Ramatsoku
Inspecting HI in a galaxy in 3D
XY
YZ
XZ
See also Punzo et al., 2015, Astronomy and Computing, 12, 86.
Overview of coherent structures in space and velocity
3D
pros:
HI Filament
CPU (OpenMP) / GPU(OpenGL) filtering techniques:
Box
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Gaussian
Intensity-driven gradient
Discovering faint structure
3D + filtering
pros:
See also Punzo et al., 2016, Astronomy and Computing, 17, 163.
Inspecting a faint HI filament in 3D
Modify interactively
the selection
Selecting ROI for
further analysis
3D
pros:
See also Punzo et al., 2017, Astronomy and Computing.
3D selection: CloudLasso
Quantitative visualization: Histogram
Quantitative visualization: Profiles
Quantitative visualization: PV Slice
Quantitative visualization: PV Diagram
Quantitative visualization
See https://github.com/Punzo/SlicerAstro/wiki
Quantitative visualization
See https://github.com/Punzo/SlicerAstro/wiki
Quantitative visualization
See https://github.com/Punzo/SlicerAstro/wiki
Quantitative visualization
See https://github.com/Punzo/SlicerAstro/wiki
Automated fitting:
See also Di Teodoro et al., 2015, MNRAS; https://github.com/editeodoro/Bbarolo
NGC 5055
Image credit: T. Oosterloo
Image credit: T. van der Hulst
Image credit: Rogstad et al. 1974
Tilted-ring model fitting
Discovering subtle structures
3D + modeling
pros:
See also Punzo et al., 2017, Astronomy and Computing.
NGC 2403
Kinematic models as masks
Visual Analytics example: manual model refinement
The main aim of SlicerAstro is to aid source inspection
and interactive analysis of spectral line data;
The 3D visualization gives an immediate overview of coherent structures in space and velocity;
The 3D visualization (coupled with filtering ad modeling) greatly helps the discovery of faint and subtle structures;
SlicerAstro source code available at:
https://github.com/Punzo/SlicerAstro
Binaries (Linux and Mac) are available in the
3DSlicer Extension Manager
http://download.slicer.org/
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C
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We thank S. Pieper (Isomics), A. Lasso (Queen's University)
and K. Martin (Kitware) for their feedback and help.
Final Remarks