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Clustering-informed cinematic astrophysical data visualization with application to the Moon-forming terrestrial synestia
Astronomy and Computing ( IF 2.5 ) Pub Date : 2020-09-09 , DOI: 10.1016/j.ascom.2020.100424
P.D. Aleo , S.J. Lock , D.J. Cox , S.A. Levy , J.P. Naiman , A.J. Christensen , K. Borkiewicz , R. Patterson

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.



中文翻译:

聚类信息的电影天体物理数据可视化及其在形成月球的陆地前体中的应用

目前,科学可视化工具尚未经过优化,无法创建用于科学交流的数字数据的电影形式,生产质量的表示形式。在产品线中的雌甾,我们概述了从原始模拟到完成的渲染的分步过程,以教会可视化领域的非专家如何自行实现生产质量的输出。我们演示了使用视觉效果软件Houdini进行电影天体物理数据可视化(通过机器学习聚类算法提供信息)的可行性。为了证明该管道的功能,我们使用了Lock等人(2018)的撞击后,热平衡的月球形成先兆。我们的方法旨在识别“可物理解释的”簇,其中在适当的相空间中识别出的簇(例如,在这里我们使用温度-熵相-空间)对应于模拟数据中具有物理意义的结构。然后,通过在简化的Houdini软件着色网络中通知颜色映射过程,可以将聚类结果用于突出显示这些结构,在该网络中,相异的相空间簇被映射到不同的颜色值,以便于视觉识别。群集信息还可以通过Houdini的“场景视图”在3D位置空间中使用,以帮助物理群集查找,仿真原型设计和数据探索。我们将基于聚类的渲染与高级可视化实验室(AVL)团队创建的渲染进行比较,以进行完整的圆顶展示“ Imagine the Moon”作为概念证明。用 有助于物理集群查找,仿真原型制作和数据探索。我们将基于聚类的渲染与高级可视化实验室(AVL)团队创建的渲染进行比较,以进行完整的圆顶展示“ Imagine the Moon”作为概念证明。用 有助于物理集群查找,仿真原型制作和数据探索。我们将基于聚类的渲染与高级可视化实验室(AVL)团队创建的渲染进行比较,以进行完整的圆顶展示“ Imagine the Moon”作为概念证明。用Estra的科学家们拥有创建自己的生产质量的,数据驱动的可视化工具。

更新日期:2020-09-09
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