Simeon Simjanovski

PhD Candidate

Simeon is a PhD Student in the Bose-Einstein Laboratory at UQ under supervision of Tyler Neely. Simeon completed his Bachelors with Honours in the group where he looked at the optimisation of vortex creation in a BEC using machine learning. Currently, he is working on turbulence instabilities and cascades in Bose-Einstein Condensates.

Physics Annexe Room 06-314 / General Purpose South Room 122a
s.simjanovski@uq.edu.au

Pre-prints

The list below includes current pre-prints published on the arXiv.
Featured
Shear-Induced Decaying Turbulence in Bose-Einstein Condensates
Shear-Induced Decaying Turbulence in Bose-Einstein Condensates

We study the creation and breakdown of a quantized vortex shear layer forming between a stationary Bose-Einstein condensate and a stirred-in persistent current. Once turbulence is established, we characterize the progressive clustering of the vortices, showing that the cluster number follows a power law decay with time, similar to decaying turbulence in other two-dimensional systems. Numerical study of the system demonstrates good agreement of the experimental data with a point vortex model that includes damping and noise. With increasing vortex number in the computational model, we observe a convergence of the power-law exponent to a fixed value.

Publications

The list below includes all publications associated with the UQ BEC lab.
Featured
Optimizing persistent currents in a ring-shaped Bose-Einstein condensate using machine learning
Optimizing persistent currents in a ring-shaped Bose-Einstein condensate using machine learning

We demonstrate a method for generating persistent currents in Bose-Einstein condensates by using a Gaussian process learner to experimentally control the stirring of the superfluid. The learner optimizes four different outcomes of the stirring process: (O.I) targeting and (O.II) maximization of the persistent current winding number and (O.III) targeting and (O.IV) maximization with time constraints. The learner optimizations are determined based on the achieved winding number and the number of spurious vortices introduced by stirring. We find that the learner is successful in optimizing the stirring protocols, although the optimal stirring profiles vary significantly depending strongly on the choice of cost function and scenario. These results suggest that stirring is robust and persistent currents can be reliably generated through a variety of stirring approaches.