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Decorrelation Of Samples In Quantum Monte Carlo Calculations And Scaling Of Autocorrelation Time In Li And H₂O Clusters, D. Nissenbaum, B. Barbiellini, A. Bansil
Decorrelation Of Samples In Quantum Monte Carlo Calculations And Scaling Of Autocorrelation Time In Li And H₂O Clusters, D. Nissenbaum, B. Barbiellini, A. Bansil
Bernardo Barbiellini
We have investigated decorrelation of samples in Quantum Monte Carlo (QMC) ground-state energy calculations for large Li and H₂O nanoclusters. Binning data as a way of eliminating statistical correlations, as is the common practice, is found to become increasingly impractical as the system size grows. We demonstrate nevertheless that it is possible to perform accurate energy calculations—without decorrelating samples—by exploiting the scaling of the integrated autocorrelation time τ as a function of the number of electrons in the system.
Scaling Behavior Of The Exchange-Bias Training Effect, Christian Binek, Srinivas Polisetty, Sarbeswar Sahoo
Scaling Behavior Of The Exchange-Bias Training Effect, Christian Binek, Srinivas Polisetty, Sarbeswar Sahoo
Christian Binek
The dependence of the exchange-bias training effect on temperature and ferromagnetic film thickness is studied in detail and scaling behavior of the data is presented. Thickness-dependent exchange bias and its training are measured using the magneto-optical Kerr effect. A focused laser beam is scanned across a Co wedge probing local hysteresis loops of the Co film which is pinned by an antiferromagnetic CoO layer of uniform thickness. A phenomenological theory is best fitted to the exchange-bias training data resembling the evolution of the exchange-bias field on subsequently cycled hysteresis loops. Best fits are done for various temperatures and Co thicknesses. …
Renormalization-Group Approach To The Critical-Behavior Of The Forest-Fire Model, V Loreto, L Pietronero, A Vespignani, S Zapperi
Renormalization-Group Approach To The Critical-Behavior Of The Forest-Fire Model, V Loreto, L Pietronero, A Vespignani, S Zapperi
Alessandro Vespignani
We introduce a renormalization scheme for the one- and two-dimensional forest-fire model in order to characterize the nature of the critical state and its scale invariant dynamics. We show the existence of a relevant scaling field associated with a repulsive fixed point. This model is therefore critical in the usual sense because the control parameter has to be tuned to its critical value in order to get criticality. It turns out that this is not just the condition for a time scale separation. The critical exponents are computed analytically and we obtain nu = 1.0, tau = 1.0 and nu …