Summary
Self-consistent estimation of three-dimensional structures is a challenging task in the analysis of astronomical data, which is predominantly two-dimensional. Obtaining explicit and scientifically justifiable spatial models is also an essential task for visualization scientists working with astronomers to develop comprehensive simulations of complex astrophysical structures and phenomena.
We address the fundamental problem of facilitating the interactive utilization of two-dimensional stellar spectral data to impose consistent constraints on three-dimensional interstellar cloud structures. This enables the construction and verification of models for spectrally absorbing clouds of gas and dust lying between the Earth and nearby stars. Our data construction tool, while specific to the astronomy application, is a prototype for the design of any expertise-driven system for resolving ambiguous initial data into a plausible complete model. In this case, we use spectral information and distances derived from stellar measurements such as the Hipparcos satellite data; the domain expert exploits the initial estimate of dust cloud thickness estimated from these data to adjust the 3D shape of the cloud. Various additional sources of information may be combined to decide on interactive changes in the parameters, yielding a qualitatively self-consistent shape model.
The examples tested highlight the importance of allowing the scientist to interact with and tune a multitude of variables and parameters in the course of the data analysis, and suggest that fully automatic tools cannot be expected to function acceptably in the construction of 3D models of interstellar media for visualization purposes.
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Hanson, A.J., Fu, CW., Frisch, P.C. (2004). Constraint-Based Astronometric Modeling Tools. In: Brunnett, G., Hamann, B., Müller, H., Linsen, L. (eds) Geometric Modeling for Scientific Visualization. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-07443-5_26
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DOI: https://doi.org/10.1007/978-3-662-07443-5_26
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