Exploring and Interrogating Astrophysical Data in Virtual Reality. (arXiv:2012.10342v3 [astro-ph.IM] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Jarrett_T/0/1/0/all/0/1">T.H. Jarrett</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Comrie_A/0/1/0/all/0/1">A. Comrie</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Marchetti_L/0/1/0/all/0/1">L. Marchetti</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Sivitilli_A/0/1/0/all/0/1">A. Sivitilli</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Macfarlane_S/0/1/0/all/0/1">S. Macfarlane</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Vitello_F/0/1/0/all/0/1">F. Vitello</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Becciani_U/0/1/0/all/0/1">U. Becciani</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Taylor_A/0/1/0/all/0/1">A. R. Taylor</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hulst_J/0/1/0/all/0/1">J.M. van der Hulst</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Serra_P/0/1/0/all/0/1">P. Serra</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Katz_N/0/1/0/all/0/1">N. Katz</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cluver_M/0/1/0/all/0/1">M. Cluver</a>
Scientists across all disciplines increasingly rely on machine learning
algorithms to analyse and sort datasets of ever increasing volume and
complexity. Although trends and outliers are easily extracted, careful and
close inspection will still be necessary to explore and disentangle detailed
behavior, as well as identify systematics and false positives. We must
therefore incorporate new technologies to facilitate scientific analysis and
exploration. Astrophysical data is inherently multi-parameter, with the
spatial-kinematic dimensions at the core of observations and simulations. The
arrival of mainstream virtual-reality (VR) headsets and increased GPU power, as
well as the availability of versatile development tools for video games, has
enabled scientists to deploy such technology to effectively interrogate and
interact with complex data. In this paper we present development and results
from custom-built interactive VR tools, called the iDaVIE suite, that are
informed and driven by research on galaxy evolution, cosmic large-scale
structure, galaxy-galaxy interactions, and gas/kinematics of nearby galaxies in
survey and targeted observations. In the new era of Big Data ushered in by
major facilities such as the SKA and LSST that render past analysis and
refinement methods highly constrained, we believe that a paradigm shift to new
software, technology and methods that exploit the power of visual perception,
will play an increasingly important role in bridging the gap between
statistical metrics and new discovery. We have released a beta version of the
iDaVIE software system that is free and open to the community.
Scientists across all disciplines increasingly rely on machine learning
algorithms to analyse and sort datasets of ever increasing volume and
complexity. Although trends and outliers are easily extracted, careful and
close inspection will still be necessary to explore and disentangle detailed
behavior, as well as identify systematics and false positives. We must
therefore incorporate new technologies to facilitate scientific analysis and
exploration. Astrophysical data is inherently multi-parameter, with the
spatial-kinematic dimensions at the core of observations and simulations. The
arrival of mainstream virtual-reality (VR) headsets and increased GPU power, as
well as the availability of versatile development tools for video games, has
enabled scientists to deploy such technology to effectively interrogate and
interact with complex data. In this paper we present development and results
from custom-built interactive VR tools, called the iDaVIE suite, that are
informed and driven by research on galaxy evolution, cosmic large-scale
structure, galaxy-galaxy interactions, and gas/kinematics of nearby galaxies in
survey and targeted observations. In the new era of Big Data ushered in by
major facilities such as the SKA and LSST that render past analysis and
refinement methods highly constrained, we believe that a paradigm shift to new
software, technology and methods that exploit the power of visual perception,
will play an increasingly important role in bridging the gap between
statistical metrics and new discovery. We have released a beta version of the
iDaVIE software system that is free and open to the community.
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