Logarithmic corrections in (4+1)-dimensional directed percolation

TL;DR

This paper provides precise estimates of critical points in (4+1)-dimensional directed percolation and analyzes logarithmic corrections, demonstrating the importance of next-to-leading terms for accurate modeling.

Contribution

The study offers the most accurate critical point estimates for 4+1D directed percolation and compares detailed logarithmic correction calculations with simulation data.

Findings

Leading logarithmic fits overestimate correction powers by 50%.

Including next-to-leading terms yields nearly perfect fits.

One observable combination appears free of logarithmic corrections.

Abstract

We simulate directed site percolation on two lattices with 4 spatial and 1 time-like dimensions (simple and body-centered hypercubic in space) with the standard single cluster spreading scheme. For efficiency, the code uses the same ingredients (hashing, histogram re-weighing, and improved estimators) as described in Phys. Rev. {\bf E 67}, 036101 (2003). Apart from providing the most precise estimates for $p_c$ on these lattices, we provide a detailed comparison with the logarithmic corrections calculated by Janssen and Stenull [Phys. Rev. {\bf E 69}, 016125 (2004)]. Fits with the leading logarithmic terms alone would give estimates of the powers of these logarithms which are too big by typically 50%. When the next-to-leading terms are included, each of the measured quantities (the average number of sites wetted at time $t$, their average distance from the seed, and the probability of cluster survival) can be fitted nearly perfectly. But these fits would not be mutually consistent. With a consistent set of fit parameters, one obtains still much improvement over the leading log - approximation. In particular we show that there is one combination of these three observables which seems completely free of logarithmic terms.