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| Paper: |
The Sherpa Maximum Likelihood Estimator |
| Volume: |
442, Astronomical Data Analysis Software and Systems XX (ADASSXX) |
| Page: |
517 |
| Authors: |
Nguyen, D.; Doe, S.; Evans, I.; Hain, R.; Primini, F. |
| Abstract: |
A primary goal for the second release of the Chandra Source Catalog
(CSC) is to include X-ray sources with as few as 5 photon
counts detected in stacked observations of the same field, while
maintaining acceptable detection efficiency and false source
rates. Aggressive source detection methods will result in detection of many false positive source candidates. Candidate detections will then be sent to a new tool, the Maximum Likelihood Estimator (MLE), to evaluate the likelihood that a detection is a real source. MLE uses the Sherpa modeling and fitting engine to fit a model of a background and source to multiple overlapping candidate source regions. A
background model is calculated by simultaneously fitting the observed photon flux in multiple background regions. This model is used to determine the quality of the fit statistic for a background-only
hypothesis in the potential source region. The statistic for a
background-plus-source hypothesis is calculated by adding a Gaussian source model convolved with the appropriate Chandra point spread function (PSF) and simultaneously fitting the observed photon flux in
each observation in the stack. Since a candidate source may be located
anywhere in the field of view of each stacked observation, a different
PSF must be used for each observation because of the strong spatial
dependence of the Chandra PSF. The likelihood of a valid source being
detected is a function of the two statistics (for background alone, and for background-plus-source). The MLE tool is an extensible Python
module with potential for use by the general Chandra user. |
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