Quantifying Variations In Multi-parameter Models With The Photon Clean Method (PCM) And Bootstrap Methods

Details Quantifying Variations In Multi-parameter Models With The Photon Clean Method (PCM) And Bootstrap Methods Quantifying Variations In Multi-parameter Models With The Photon Clean Method (PCM) And Bootstrap Methods

May 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007aas...210.4304c&link_type=abstract

American Astronomical Society Meeting 210, #43.04; Bulletin of the American Astronomical Society, Vol. 39, p.157

Statistics

Methodology

Scientific paper

We present examples of an analysis progression consisting of a synthesis of the Photon Clean Method (Carpenter, Jernigan, Brown, Beiersdorfer 2007) and bootstrap methods to quantify errors and variations in many-parameter models. The Photon Clean Method (PCM) works well for model spaces with large numbers of parameters proportional to the number of photons, therefore a Monte Carlo paradigm is a natural numerical approach. Consequently, PCM, an "inverse Monte-Carlo" method, requires a new approach for quantifying errors as compared to common analysis methods for fitting models of low dimensionality. This presentation will explore the methodology and presentation of analysis results derived from a variety of public data sets, including observations with XMM-Newton, Chandra, and other NASA missions. Special attention is given to the visualization of both data and models including dynamic interactive presentations. This work was performed under the auspices of the Department of Energy under contract No. W-7405-Eng-48. We thank Peter Beiersdorfer and Greg Brown for their support of this technical portion of a larger program related to science with the LLNL EBIT program.

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