Seminar on Computational Learning and Adaptation

  Hierarchies of Models: Toward Understanding Planetary Nebulae

Kevin H. Knuth
NASA Ames Research Center,
Computational Sciences Department, Code IC
Moffett Field CA 94035 USA

Stars like our sun (initial masses between 0.8 to 8 solar masses) end their lives as swollen red giants surrounded by cool extended atmospheres. The nuclear reactions in their cores create carbon, nitrogen and oxygen, which are transported by convection to the outer envelope of the stellar atmosphere. As the star finally collapses to become a white dwarf, this envelope is expelled from the star to form a planetary nebula (PN) rich in organic molecules. The physics, dynamics, and chemistry of these nebulae are poorly understood and have implications not only for our understanding of the stellar life cycle but also for organic astrochemistry and the creation of prebiotic molecules in interstellar space.

We are working toward generating three-dimensional models of planetary nebulae (PNe), which include the size, orientation, shape, expansion rate and mass distribution of the nebula. Such a reconstruction of a PN is a challenging problem for several reasons. First, the data consist of images (taken from a single viewpoint) obtained over time from the Hubble Space Telescope (HST) and ground-based long-slit spectra. Second, the fact that we have two disparate data types requires that we utilize a method that allows these data to be used together to obtain a solution. To address these first two challenges we employ Bayesian model estimation using a parameterized physical model that incorporates much prior information about the known physics of the PN. As we have found that the forward problem of this comprehensive model is extremely time consuming, we have introduced a method relying on a hierarchical set of models, where each model in the hierarchy allows us to estimate increasingly more detail. In this talk, we describe these analysis techniques and explore the advantages and disadvantages of employing such a set of hierarchical models.

This talk describes joint work with Arsen R. Hajian.

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