8th HEAD Meeting, 8-11 September, 2004
Session 16 Missions, Instruments and Data Analysis
Poster, Thursday, September 9, 2004, 9:00am-10:00pm

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[16.31] Detecting Variations in Source Intensity for Poisson Data

Y. Yu, X.L. Meng (Dept. of Statistics, Harvard Univ.), D. Van Dyk (Dept. of Statistics, UC Irvine), V. Kashyap, A. Zezas (Smithsonian Astrophysical Observatory, Harvard Univ.)

The analysis of light curves is an especially challenging statistical task for low-count Poisson data: Variation in the source intensity may be shadowed by the Poisson variation of the counts. Here we discuss a class of statistical models that are designed to capture trends and autocorrelations in Poisson data that are collected over time. We expect such models to be useful not only in the analysis of sources that vary in their intensities but also in joint spectral-temporal modeling tasks such as analyzing the variability in spectral power law parameters or hardness ratios over time. To tackle these problems, we propose a Bayesian statistical model that takes into account both the variation in source intensity and the Poissonian character of the count data. Parameter estimation and error bars are computed using sophisticated MCMC (Markov chain Monte Carlo) methods. We illustrate our methods using several X-ray sources.

The authors gratefully acknowledge funding for this project partially provided by NSF grant DMS-01-04129 and by NASA Contract NAS8-39073 (Chandra X-ray Center).

The author(s) of this abstract have provided an email address for comments about the abstract: yu@stat.harvard.edu

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Bulletin of the American Astronomical Society, 36 #3
© 2004. The American Astronomical Soceity.