A Neural Network Second Level Trigger for the H1-Experiment at HERA

Abstract

At the HERA ep collider the expected machine background rates exceed by several orders of magnitude the typical collision rates from ep physics reactions. One of the great challenges for the ep experiments at HERA is therefore to find methods suppressing the high rate machine background efficiently without cutting heavily into the physics. We present here the concept, the design, and the status for a second level hardware trigger based on the neural network architecture. We address the problems of efficiently selecting "known" physics as well as preparing such a trigger for "new" physics never presented to the network and give a few examples for networks specifically trained for physical reactions close to the background. The training is done with real data and the corresponding nets are ready for installation in the network trigger, which is expected to start operation in the fall of 1995. 1 Introduction At full luminosity, the HERA Electron-Proton-Collider at DESY will challenge e..