Speaker
Description
In Lattice QCD, standard Markov Chain Monte Carlo (MCMC) algorithms exhibit topological freezing: As the continuum limit is approached, the autocorrelation times of topological observables increase exponentially. As a result, ergodicity is effectively lost and statistical error estimates become unreliable. A method to mitigate this is Parallel Tempering on Boundary Conditions (PTBC). The algorithm introduces multiple Markov chains: one samples the target distribution and the rest sample modified distributions with progressively shorter autocorrelation times for topological observables. The chains are evolved in parallel using standard MCMC methods, periodically proposing swaps between neighboring chains, enabling configurations to diffuse toward the target chain, satisfying ergodicity. In this talk, we present a study of the different components of the algorithm and their impact on topological observables in SU(3) pure gauge theory.