Cosmological Experiments in Superfluids and Superconductors

Abstract

Evolution of the order parameter in condensed matter analogues of cosmological phase transitions is discussed. It is shown that the density of the frozen-out topological defects is set by the competition between the quench rate - the rate at which the phase transition is taking place - and the relaxation rate of the order parameter. More specifically, the characteristic domain size which determines the typical distance separating topological defects in the new broken symmetry phase (and, therefore, their density) is determined by the correlation length at the instant at which the relaxation timescale of the order parameter is equal to the time from the phase transition. This scenario shares with the Kibble mechanism the idea that topological defects will appear “in between” domains with independently chosen broken symmetry vacuum. However, it differs from the original proposal in estimating the size of such domains through the non-equilibrium aspects of the transition (quench rate), rather than through the Ginzburg temperature at which thermally activated symmetry restoration can still occur in the correlation - length sized volumes of the broken symmetry phase. This scenario can be employed to analyze recent superfluid quench experiments carried out in bulk He4 to study the analogue of the “cosmological” prediction of significant vortex line production. It can be also applied to superfluid quenches in annular geometry, as well as to the rapid phase transition from the normal metal to superconductor, where the symmetry breaking occurs in the order parameter with the local (rather than a global) gauge. Cosmological implications of the revised defect formation scenario with the critical domain size set by the freeze-out time rather than by the Ginzburg temperature are also briefly considered.KeywordsCorrelation LengthGauge FieldCooper PairCosmic StringVortex LineThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.