Large N Expansion for 4-Epsilon Dimensional Oriented Manifolds in Random Media

Physics – Condensed Matter

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

23 pages + 4 figures available upon request, Plain TeX

Scientific paper

10.1103/PhysRevB.48.5949

The equilibrium statistical mechanics of a d dimensional ``oriented'' manifold in an N+d dimensional random medium are analyzed in d=4-epsilon dimensions. For N=1, this problem describes an interface pinned by impurities. For d=1, the model becomes identical to the directed polymer in a random medium. Here, we generalize the functional renormalization group method used previously to study the interface problem, and extract the behavior in the double limit epsilon small and N large, finding non-analytic corrections in 1/N. For short-range disorder, the interface width scales as a power law of the width. We calculate the roughness exponent characterizing this power law for small epsilon and large N, as well as other properties of the phase. We also analyze the behavior for disorder with long-range correlations, as is appropriate for interfaces in random field systems, and study the crossover between the two regimes.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Large N Expansion for 4-Epsilon Dimensional Oriented Manifolds in Random Media does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Large N Expansion for 4-Epsilon Dimensional Oriented Manifolds in Random Media, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Large N Expansion for 4-Epsilon Dimensional Oriented Manifolds in Random Media will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-133861

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.