TY - JOUR
T1 - Exploring and interrogating astrophysical data in virtual reality
AU - Jarrett, T. H.
AU - Comrie, A.
AU - Marchetti, L.
AU - Sivitilli, A.
AU - Macfarlane, S.
AU - Vitello, F.
AU - Becciani, U.
AU - Taylor, A. R.
AU - van der Hulst, J. M.
AU - Serra, P.
AU - Katz, N.
AU - Cluver, M. E.
N1 - Funding Information:
Special thanks to Trystan Lambert, Nathan Deg, Meridith Joyce, Claude Carignan, Gyula Jozsa, Marcin Glowacki, Julia Healy, and Charl Cater for assisting with testing and validation of the iDaVIE system. We thank Stéphane Courteau, Mike Hudson, Mark Subbarao, Jayanne English, Miguel Aragon-Calvo, Mark Neyrinck and Chris Fluke for insightful discussion on the future of VR and visualisation in data science. We thank our interdisciplinary colleagues Ben Loos and Andre Du Toit for fascinating visualisations from the very small (molecular biology) angular-scale world. Special thanks to Joseph Eakin for wonderful technical service during our VR-to-Dome demonstration at Colgate University. And finally a huge thanks to the two referees who provided a number of excellent suggestions and critiques that helped sharpen the manuscript. THJ acknowledges funding from the National Research Foundation, South Africa under the Research Career Advancement and South African Research Chair Initiative programs (SARChI) , respectively. This work made use of the Inter-University Institute for Data Intensive Astronomy (IDIA) Visualization Lab. 13 13 IDIA is a partnership of the University of Cape Town, the University of Pretoria and the University of the Western Cape. The authors acknowledge financial support from the Italian Ministry of Foreign Affairs and International Cooperation (MAECI Grant Number ZA18GR02 ) and the South African NRF (Grant Number 113121 ) as part of the ISARP RADIOSKY2020 Joint Research Scheme. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 679627 ; project name FORNAX).
Funding Information:
Special thanks to Trystan Lambert, Nathan Deg, Meridith Joyce, Claude Carignan, Gyula Jozsa, Marcin Glowacki, Julia Healy, and Charl Cater for assisting with testing and validation of the iDaVIE system. We thank St?phane Courteau, Mike Hudson, Mark Subbarao, Jayanne English, Miguel Aragon-Calvo, Mark Neyrinck and Chris Fluke for insightful discussion on the future of VR and visualisation in data science. We thank our interdisciplinary colleagues Ben Loos and Andre Du Toit for fascinating visualisations from the very small (molecular biology) angular-scale world. Special thanks to Joseph Eakin for wonderful technical service during our VR-to-Dome demonstration at Colgate University. And finally a huge thanks to the two referees who provided a number of excellent suggestions and critiques that helped sharpen the manuscript. THJ acknowledges funding from the National Research Foundation, South Africa under the Research Career Advancement and South African Research Chair Initiative programs (SARChI), respectively. This work made use of the Inter-University Institute for Data Intensive Astronomy (IDIA) Visualization Lab.13 https://vislab.idia.ac.za. IDIA is a partnership of the University of Cape Town, the University of Pretoria and the University of the Western Cape. The authors acknowledge financial support from the Italian Ministry of Foreign Affairs and International Cooperation (MAECI Grant Number ZA18GR02) and the South African NRF (Grant Number 113121) as part of the ISARP RADIOSKY2020 Joint Research Scheme. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 679627; project name FORNAX).
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/10
Y1 - 2021/10
N2 - 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 behaviour, 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.
AB - 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 behaviour, 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.
KW - 3D catalogues
KW - Data visualisation
KW - Radio astrophysics
KW - Virtual reality
KW - Volumetric rendering
UR - https://www.scopus.com/pages/publications/85116926035
U2 - 10.1016/j.ascom.2021.100502
DO - 10.1016/j.ascom.2021.100502
M3 - Article
AN - SCOPUS:85116926035
SN - 2213-1337
VL - 37
JO - Astronomy and Computing
JF - Astronomy and Computing
M1 - 100502
ER -