SELF-AVOIDING RANDOM-WALKS AS A PROBE OF LARGE-SCALE STRUCTURE IN THE UNIVERSE

We present a new random walk technique for probing large-scale structure in the Universe. The method is based upon a growth model used in condensed matter physics. We have applied our algorithm to point distributions in two and three dimensions. We find that our method can distinguish between distributions that have similar two-point correlation functions on small scales, but which have different amounts of large-scale power.