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                                                              LU TP 88-8
                                                              April 1988


                TRACK FINDING WITH NEURAL NETWORKS


                       Carsten Peterson

         Department of Theoretical Physics, University of Lund,
              Solvegatan 14A, S-223 62 Lund, Sweden


           [Submitted to Nuclear Instrumentation Methods]



ABSTRACT (a modified version):

In high energy physics experiments the produced particles give rise to sparks
or signals that follow their tracks. In events with large multiplicity
reconstructing the tracks from the signals poses a computationally intensive
task. Until now signal data has been collected on tapes and then processed
with conventional CPU power. In future accelerators like SSC real time
experimental triggers will be needed that would benefit from immediate
track finding.

The track finding problem is a combinatorial optimization problem. We have
cast this problem onto a neural network by letting a neuron represent whether
a line segment between two signals exist or not. We have applied mean field
theory equations together with a greedy heuristic to planar situations with
very encouraging results with respect to the quality of the solutions. Also
rapid convergence times and good scaling properties are found. The
generalization to three dimensions is straightforward.

With the great potential that exists for realizing the neural network
technology in custom made hardware we feel that this approach to the
track finding problem could be very important in the future for experimental
high energy physics. Our approach to track finding is generic. Many other and
less "peaceful" applications than tracking elementary particles could easily be
imagined.



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