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Clustering-informed cinematic astrophysical data visualization with application to the Moon-forming terrestrial synestia

Aleo, P. D. and Lock, S. J. and Cox, D. J. and Levy, S. A. and Naiman, J. P. and Christensen, A. J. and Borkiewicz, K. and Patterson, R. (2020) Clustering-informed cinematic astrophysical data visualization with application to the Moon-forming terrestrial synestia. Astronomy and Computing, 33 . Art. No. 100424. ISSN 2213-1337. https://resolver.caltech.edu/CaltechAUTHORS:20200916-073414104

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Abstract

Scientific visualization tools are currently not optimized to create cinematic, production-quality representations of numerical data for the purpose of science communication. In our pipeline Estra, we outline a step-by-step process from a raw simulation into a finished render as a way to teach non-experts in the field of visualization how to achieve production-quality outputs on their own. We demonstrate feasibility of using the visual effects software Houdini for cinematic astrophysical data visualization, informed by machine learning clustering algorithms. To demonstrate the capabilities of this pipeline, we used a post-impact, thermally-equilibrated Moon-forming synestia from Lock et al., (2018). Our approach aims to identify “physically interpretable” clusters, where clusters identified in an appropriate phase space (e.g. here we use a temperature–entropy phase–space) correspond to physically meaningful structures within the simulation data. Clustering results can then be used to highlight these structures by informing the color-mapping process in a simplified Houdini software shading network, where dissimilar phase–space clusters are mapped to different color values for easier visual identification. Cluster information can also be used in 3D position space, via Houdini’s Scene View, to aid in physical cluster finding, simulation prototyping, and data exploration. Our clustering-based renders are compared to those created by the Advanced Visualization Lab (AVL) team for the full dome show “Imagine the Moon” as proof of concept. With Estra, scientists have a tool to create their own production-quality, data-driven visualizations.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.ascom.2020.100424DOIArticle
ORCID:
AuthorORCID
Aleo, P. D.0000-0002-6298-1663
Lock, S. J.0000-0001-5365-9616
Naiman, J. P.0000-0002-9397-6189
Christensen, A. J.0000-0002-8711-2317
Borkiewicz, K.0000-0002-3748-231X
Additional Information:© 2020 Elsevier B.V. Received 16 July 2020, Accepted 30 August 2020, Available online 9 September 2020.
Funders:
Funding AgencyGrant Number
NSFEAR-1947614
Jerry FiddlerUNSPECIFIED
Caltech Division of Geological and Planetary SciencesUNSPECIFIED
Subject Keywords:Methods: Data analysis; Methods: Numerical
Record Number:CaltechAUTHORS:20200916-073414104
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200916-073414104
Official Citation:P.D. Aleo, S.J. Lock, D.J. Cox, S.A. Levy, J.P. Naiman, A.J. Christensen, K. Borkiewicz, R. Patterson, Clustering-informed cinematic astrophysical data visualization with application to the Moon-forming terrestrial synestia, Astronomy and Computing, Volume 33, 2020, 100424, ISSN 2213-1337, https://doi.org/10.1016/j.ascom.2020.100424. (http://www.sciencedirect.com/science/article/pii/S2213133720300780)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105395
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:16 Sep 2020 15:42
Last Modified:16 Sep 2020 15:42

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