Track finding with neural networks
A neural network algorithm for finding tracks in high energy physics experiments is presented. The performance of the algorithm is explored on modest size samples with encouraging results. It is inherently parallel and thus suitable for execution on a conventional SIMD architecture. More important, it naturally lends itself to direct implementations in custom made hardware, which would permit real
