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Astrostatistical Tools in Python
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| Astronomers are developing new and powerful statistical methods in response to the recent explosion in astronomical data quantity and quality. These methods promise to significantly increase the amount and quality of science distilled from complex data. Though powerful and computationally complex, most new methods are conceptually straightforward and can be used without knowledge of implementation details. Our projects primary objective is to enable astronomers to use sophisticated methods without requiring them to master the art of statistical computing. To accomplish this, we will pursue four subsidiary objectives:
(1) Within a unifying framework, implement a broad and deep collection of statistical software tools built on algorithms from recent and current research in astrostatistics and computational statistics, wit extensive and accessible documentation. These tools will span may problem domains (e.g., time series, surveys, spectroscopy, imaging), and will offer choices of various approaches wherever possible (e.g., conventional frequentist methods and Bayesian counterparts) so users can easily compare competing methods. A parametric inference engine will supplement basic tools with a framework implementing functionality common to many parameter estimation methods (e.g., constrained optimization, multidimensional exploration and integration, Monte Carlo methods).
(2) Exploit the capabilities of a modern, object-oriented computer language Pythonto implement the tools efficiently and in a form that is seductively easy to use despite the sophistication of the underlying algorithms. Python allows simplified, high-level interfaces to methods with little compromise in computational efficiency. It is being used to rewrite the widely-used IRAF data analysis environment, so IRAF users will have particularly easy access to our tools.
(3) Undertake an outreach program to inform astronomers of the methods and tools by demonstration of their scientific utility. This will include high-visibility presentations at major astronomical meetings demonstrating use of our methods.
(4) Enable collaboration between astrostatisticians and computer scientists to ensure the combined scientific and computational quality of the tools. This project will allow astrostatisticians and a leading architect of Pythons numerical capability to devote significant effort to constructing a robust, well-documented tool kit. |
Bibliography
| | Search for High-Frequency Periodicities in Time-tagged Event Data from Gamma-Ray Bursts and Soft Gamma Repeaters
Kruger; Loredo; Wasserman; ApJ, 576 pp. 932
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