ITR/IM: Statistical Data Mining for Cosmology

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Scientists are now confronted with many very large high-quality data sets. The potential scientific benefits of these data are offset by the laborious process of analyzing them to answer questions and test theories. This project will develop new data mining algorithms in pursuit of the goal of computer assisted discovery. Two key issues in achieving this are computational efficiency and autonomy. If scientists are to focus their energy on understanding, answers must arrive in minutes rather than days, hence the need for efficiency. Autonomy is important both from the data mining and the statistical perspective. Detailed searches for relationships, models, and parameters are too large for humans to undertake manually. New statistical methods will have to autonomously and quickly select models, test their significance, and report the results to search algorithms looking for new discoveries.The National Virtual Observatory (NVO) currently under construction is a model of the future of science. The NVO will assemble petabytes of data from many multi-wavelength sky surveys into a single repository. The new methods to be developed will be implemented in the domain of cosmology, but they will be applicable to all other sciences. The members of this project are computer scientists, physicists and statisticians who have a track record of collaborating closely. Working together they have produced: new algorithmic theory, new statistical theory, and publicly fielded software packages resulting from the theory, while developing new courseware and training students.This proposal involves research and education in the following areas:Nonparametric data analysis. Nonparametric statistical models enable powerful analysis techniques that make minimal assumptions, which is critical for scientific accuracy.Automated discovery. Statistical models can be used directly for discovery. Individual objects are compared to models to identify anomalies and data generated models are compared to theoretical models to refute or confirm hypotheses.Computational methods for fast analysis. The project will build on past successes of getting orders of magnitude speedups on operations such as Expectation Maximization based clustering and n-point correlations to make the new methods fast.Automated simulation parameter searching. Using all of the above methods, a system will be developed that starts with a parameterized simulation and some observational data. The system will search the space of parameters, testing the resulting simulation against the real data using nonparametric methods to determine the best settings.