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Title:Estra: Visualizing the Moon-forming synestia informed by machine learning clustering
Author(s):Aleo, Patrick D.
Subject(s):Astronomy
Abstract:For thousands of years, humanity has described the movement and wonder of the night sky through stories. These stories catalyzed research in both observational and theoretical domains of astronomical sciences, and have sprung forth the golden age of astrophysical computation, simulation, and visualization of today. My project, Estra, enables scientists to become their own storyteller through automating, informing, and improving astrophysical data visualization using machine learning algorithms. This approach utilizes “physically interpretable” clusters—clusters corresponding to physically meaningful structures within the simulation data—to inform the color mapping transfer function, in addition to building a simple yet powerful shading network to map opacity, brightness, falloff, and other attributes. ​ Here is a demonstration of Estra applied to the Moon-forming synestia: a super-coronation limit body resulting from the high-energy, high-angular momentum impact of a celestial protoplanetary body with Earth, from which the Moon coalesces. The hot, dense core radiates vividly with a white/yellow hue, and is surrounded by a ring of cooler condensates and material. The synestia hypothesis explains the physical and chemical properties of the Moon, and this visualization provides a possible representation of our shared history billions of years ago.​
Issue Date:2020
Type:Text
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URI:http://hdl.handle.net/2142/106778
Rights Information:Copyright 2020 Patrick D. Aleo
Date Available in IDEALS:2020-04-13


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