Collaborative Research: Highly Structured Models and Statistical Computation in High-Energy Astrophysics

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Pricipal Investigators: David Van Dyk and Xiao-Li MengInstitutions: UC Riverside and Harvard UniversityCollaborative Research: Highly Structured Models and Statistical Computation in High-Energy AstrophysicsAbstractThe California-Harvard Astrostatistics Collaboration aims to designand implement fully model-based methods of statistical inference tosolve outstanding data analytic problems in high-energyastrophysics. The Collaboration's methods explicitly model thecomplexities of both astronomical sources and the data generationmechanisms inherent in new high-tech instruments and fully utilize theresulting highly structured models in learning about the underlyingastronomical and physical processes. Using these models requiressophisticated scientific computation, advanced methods for statisticalinference, and careful model checking procedures. The PIs of theCollaboration (van Dyk and Meng) both have substantial researchexperience in developing the methods that the Collaboration isextending, employing, and publicizing: inferential and efficientcomputational methods under highly-structured models that involvemultiple levels of latent variables and incomplete data. Such modelsare ideally suited to account for the many physical and instrumentalfilters that compose the data generation mechanism in high-energyastrophysics. The five consultants on the project (Chiang, Connors,Kashyap, Karovska, and Siemiginowska) all have expertise on theinstrumentation and science of high-energy astrophysics, and, all havecollaborated with statisticians in efforts to develop appropriatemethods to address scientific questions. There are two primary impactsof this project: the impact of the development of more reliablestatistical methods on scientific findings in astronomy and the impactof the new statistical inference and computation methods in a widerange of scientific fields. As the Collaboration develops methods anddistributes free software for specific inferential tasks, it alsoeducates the astronomical community as to the benefit of careful useof sophisticated statistical methods. (The Collaboration organizes oneor two special sessions at meetings of the American AstronomicalSociety each year.) It is expected that a fundamental impact of theproposed research will be a more general acceptance and more prevalentuse of appropriate methods among astronomers. Second, theCollaboration is an example of a new mode of statisticalinference. Rather than using off-the-shelf models and methods, it isbecoming ever more feasible to develop application specific modelsthat are designed to account for the particular complexities of aproblem at hand. The Collaboration develops inferential andcomputational methods for handling such multi-level models. Asapplication specific multi-level models become more prevalent, thesemethods will have application throughout the natural, social, andengineering sciences.In recent years, there has been an explosion of new data inobservational high-energy astrophysics. Recently launched orsoon-to-be launched space-based telescopes that are designed to detectand map ultra-violet, X-ray, and gamma-ray electromagnetic emissionare opening a whole new window to study the cosmos. Because theproduction of high-energy electromagnetic emission requirestemperatures of millions of degrees and is an indication of therelease of vast quantities of stored energy, these instruments give acompletely new perspective on the hot and turbulent regions of theuniverse. The new instrumentation allows for very high resolutionimaging, spectral analysis, and time series analysis. The ChandraX-ray Observatory, for example, produces images at least thirty timessharper than any previous X-ray telescope. The complexity of theinstruments, the complexity of the astronomical sources, and thecomplexity of the scientific questions leads to a subtle inferenceproblem that requires sophisticated statistical tools. For example,data are subject to non-uniform censoring, errors in measurement, andbackground contamination. Astronomical sources exhibit complex andirregular spatial structure. Scientists wish to draw conclusions as tothe physical environment and structure of the source, the processesand laws which govern the birth and death of planets, stars, andgalaxies, and ultimately the structure and evolution of theuniverse. Nonetheless little effort has been made to bring thestrength of modern statistical methods to bare on these problems. TheCalifornia-Harvard Astrostatistics Collaboration develops statisticalmethods, computational techniques, and freely available software toaddress outstanding inferential problems in high-energy astrophysics.The methods developed are an example of a new mode of statisticalinference: Rather than using off-the-shelf methods, it is becomingever more feasible to develop methods that are application specificand are designed to account for the particular complexities of aproblem at hand. The inferential and computational methods designed bythe Collaboration for handling such multi-level models haveapplication throughout the natural, social, and engineering sciences.