Proceedings Article | 17 September 2007
Proc. SPIE. 6700, Mathematics of Data/Image Pattern Recognition, Compression, Coding, and Encryption X, with Applications

KEYWORDS: Sensors, Telescopes, Gamma radiation, Absorption, Compton scattering, Space telescopes, Cameras, Pattern recognition, Kinematics, Doppler effect

The next generation of Compton telescopes (such as MEGA or NCT) will detect impinging gamma rays by
measuring one or more Compton interactions, possibly electron tracks, and a final photo absorption. However,
the recovery of the original parameters of the photon, especially its energy and direction, is a challenging task,
since the measured data only consists of a set of energy and position measurements and their ordering, i.e. the
path of the photon, is unknown. Thus the main tasks of the pattern recognition algorithm are to identify the
interaction sequence of the photon (i.e. which hit is the start point) and distinguish the pattern from background
signatures, especially incompletely absorbed events.
The most promising approach up to now is based on Bayesian statistics: The Compton interactions are
parameterized in a multi-dimensional data space, which contains the interaction information of the Compton
sequence as well as geometry information of the detector. For each data space cell the probability that the
corresponding interaction sequence is one of a correctly ordered, completely absorbed source photon can be
determined by Bayesian statistics and detailed simulations. This probability can then be used to distinguish
source photons from incompletely absorbed photons.
Simulations show that the Bayesian approach can improve the 68% event containment of the ARM distribution
by up to 40%, and results in a much better separation between "good" and "bad" events. In addition, sensitivity
improvements up to a factor 1.7 can be achieved.