List-Mode Likelihood Imaging Applied to COMPTEL Data

Details List-Mode Likelihood Imaging Applied to COMPTEL Data List-Mode Likelihood Imaging Applied to COMPTEL Data

Mar 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008head...10.0404z&link_type=abstract

American Astronomical Society, HEAD meeting #10, #4.04

Computer Science

Performance

Scientific paper

Eight years after de-orbiting CGRO, COMPTEL's 1-30 MeV all-sky imaging performance, as well as its sensitivity for continuum sources, remain unsurpassed. Moreover, currently no official successor mission exists
that might challenge COMPTEL's performance --- only GLAST is expected to improve upon COMPTEL above 20 MeV. Since the time when the original COMPTEL data analysis techniques were developed in the 1990's, the performance of state-of-the-art computers has increased by more than a factor of 100, allowing for new analysis techniques that were unthinkable at that time. These encompass detailed orbital background simulations including detector activation, Bayesian event selection techniques, and list-mode imaging.
In this work we concentrate on the list-mode maximum likelihood expectation maximization (ML-EM) imaging method. It allows all measured information to be included into the imaging response of the instrument. As a consequence, this approach has the capability to produce improved images compared to those from the original techniques applied to COMPTEL data. We are currently in the process of adapting the list-mode approach to COMPTEL, determining the imaging response via simulations, and reanalyzing data with the ML-EM method.
We will show first results of the Galactic anti-center region, and compare them to previous COMPTEL results.

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