Neural network classification of questionable EGRET events

Details Neural network classification of questionable EGRET events Neural network classification of questionable EGRET events

Feb 1992

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1992nascp3137..137m&link_type=abstract

In its The Compton Observatory Science Workshop p 137-144 (SEE N92-21874 12-90)

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Algorithms, Classifications, Coefficients, Computation, Expert Systems, Gamma Ray Astronomy, Gamma Ray Observatory, Gamma Ray Telescopes, Gamma Rays, Neural Nets, Spark Chambers, Data Bases, Feedforward Control, Loci

Scientific paper

High energy gamma rays (greater than 20 MeV) pair producing in the spark chamber of the Energetic Gamma Ray Telescope Experiment (EGRET) give rise to a characteristic but highly variable 3-D locus of spark sites, which must be processed to decide whether the event is to be included in the database. A significant fraction (about 15 percent or 104 events/day) of the candidate events cannot be categorized (accept/reject) by an automated rule-based procedure; they are therefore tagged, and must be examined and classified manually by a team of expert analysts. We describe a feedforward, back-propagation neural network approach to the classification of the questionable events. The algorithm computes a set of coefficients using representative exemplars drawn from the preclassified set of questionable events. These coefficients map a given input event into a decision vector that, ideally, describes the correct disposition of the event. The net's accuracy is then tested using a different subset of preclassified events. Preliminary results demonstrate the net's ability to correctly classify a large proportion of the events for some categories of questionables. Current work includes the use of much larger training sets to improve the accuracy of the net.

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