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We present an optimized and physically motivated method for separating top quark signal events from background events at the Tevatron. For the top quark signal t ¯ t → e/µ + 4 jets, we show how to reject all but 25% of the background in a data sample while retaining 80% of the signal, without introducing bias into the subsequent mass measurement. The(More)
The use of neural networks for signal vs. background discrimination in high-energy physics experiment has been investigated and has compared favorably with the efficiency of traditional kinematic cuts. Recent work in top quark identification produced a neural network that, for a given top quark mass, yielded a higher signal to background ratio in Monte(More)
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