Highly Structured Models and High-energy Astrophysics

• Activity Number: 59
• Type: Invited
• Date/Time: Sunday, August 8, 2004 : 6:00 PM to 7:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): David A. van Dyk*+
• Companies: University of California, Irvine
• Keywords: astronomy ; MCMC ; Bayesian methods ; missing data ; image analysis ; deconvolution

Abstract

In recent years, there has been a giant leap in the quality and quantity of new data in observational high-energy astrophysics. Recently launched or soon-to-be launched space-based telescopes that are designed to detect and map ultra-violet, X-ray, and gamma-ray electromagnetic emission are opening a whole new window to study the cosmos. Because production of high-energy electromagnetic emission requires temperatures of millions of degrees and indicates the release of vast quantities of stored energy, these instruments give a new perspective on the hot and turbulent regions of the universe. The new instrumentation allows for very high-resolution imaging, spectral analysis, and time-series analysis. The Chandra X-ray Observatory, for example, produces images at least thirty times sharper than any previous X-ray telescope. The complexity of the instruments, of the astronomical sources, and of the scientific questions leads to a subtle inference problem that require sophisticated statistical tools. This poster describes the statistical methods developed by the California-Harvard Astrostatistics Collaboration to address outstanding inferential problems in high-energy astrophysics.