Statistics – Computation
Scientific paper
Jan 2009
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009aas...21321106b&link_type=abstract
American Astronomical Society, AAS Meeting #213, #211.06; Bulletin of the American Astronomical Society, Vol. 41, p.283
Statistics
Computation
Scientific paper
Many problems in astronomy take the form of observations on some process determined by unknown physical parameters, with the relationship between the parameters and the observed quantities being defined by simulations, by a lookup table, or by a many-to-one function. For example, photometric data on a collection of stars can be used to infer the mass, age, and metallicity of those stars. This requires the analyst to specify a mapping between the parameters (mass, age, metallicity) and the observed data. Typically this is done through a lookup table created by one of many competing models of the underlying physics. In the case of Color-Magnitude Diagrams (CMDs) the mapping is many-to-one and, hence, non-invertible. This poses a number of theoretical and computational challenges for the data analysis. We present an example of these challenges in the use of CMDs to infer star formation histories, via Hierarchical Bayesian modelling. Many recent advances in computational methods and computing power enable us to answer previously infeasible questions, and to frame them in greater generality. We present our methodology for the CMD example, and indicate how the theoretical framework and computational techniques involved can be utilized in many applications beyond CMD analysis.
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