Normalized entropy density of the 3D 3-state Potts model

Using a multicanonical Metropolis algorithm we have performed Monte Carlo simulations of the 3D 3-state Potts model on $L^3$ lattices with L=20, 30, 40, 50. Covering a range of inverse temperatures from $\beta_{\min}=0$ to $\beta_{\max}=0.33$ we calculated the infinite volume limit of the entropy density $s(\beta)$ with its normalization obtained from $s(0)=\ln 3$. At the transition temperature the entropy and energy endpoints in the ordered and disordered phase are estimated employing a novel reweighting procedure. We also evaluate the transition temperature and the order-disorder interface tension. The latter estimate increases when capillary waves are taken into account.Comment: 5 pages, 4 figure

Clopper-Pearson Bounds from HEP Data Cuts

For the measurement of $N_s$ signals in $N$ events rigorous confidence bounds on the true signal probability $p_{\rm exact}$ were established in a classical paper by Clopper and Pearson [Biometrica 26, 404 (1934)]. Here, their bounds are generalized to the HEP situation where cuts on the data tag signals with probability $P_s$ and background data with likelihood $P_b<P_s$. The Fortran program which, on input of $P_s$, $P_b$, the number of tagged data $N^Y$ and the total number of data $N$, returns the requested confidence bounds as well as bounds on the entire cumulative signal distribution function, is available on the web. In particular, the method is of interest in connection with the statistical analysis part of the ongoing Higgs search at the LEP experiments

Biased Metropolis Sampling for Rugged Free Energy Landscapes

Metropolis simulations of all-atom models of peptides (i.e. small proteins) are considered. Inspired by the funnel picture of Bryngelson and Wolyness, a transformation of the updating probabilities of the dihedral angles is defined, which uses probability densities from a higher temperature to improve the algorithmic performance at a lower temperature. The method is suitable for canonical as well as for generalized ensemble simulations. A simple approximation to the full transformation is tested at room temperature for Met-Enkephalin in vacuum. Integrated autocorrelation times are found to be reduced by factors close to two and a similar improvement due to generalized ensemble methods enters multiplicatively.Comment: Plenary talk at the Los Alamos conference, The Monte Carlo Method in Physical Sciences: Celebrating the 50th Anniversary of the Metropolis Algorithm, to appear in the proceedings, 11 pages, 4 figures, one table. Inconsistencies corrected and references adde