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8–13 Jun 2025
America/Regina timezone
Welcome to the 2025 CAP Congress Program website! / Bienvenue au siteweb du programme du Congrès de l'ACP 2025!

Tunable domain-wall dynamics in multi-domain spin structures in an ultracold 87Rb gas

10 Jun 2025, 17:15
15m
Rm 208 (cap.52) (Arts Bldg., U.Sask.)

Rm 208 (cap.52)

Arts Bldg., U.Sask.

Oral Competition (Graduate Student) / Compétition orale (Étudiant(e) du 2e ou 3e cycle) Atomic, Molecular and Optical Physics, Canada / Physique atomique, moléculaire et photonique, Canada (DAMOPC-DPAMPC) (DAMOPC) T3-3 Rydberg Atoms and Radiative Transfer | Atomes de Rydberg et transferts radiatifs (DPAMPC)

Speaker

Olha Farion (Simon Fraser University)

Description

We study domain wall motion in pseudo-spin-½ ultracold 87Rb gas initialized in an ‘up-down-up’ configuration, with helical domain walls between the regions of different magnetization. The interplay between diffusive pressure and induced spin-currents due to spin-exchange collisions leads to complex domain-wall dynamics. We qualitatively distinguish two regimes of wall motion. At short times, transverse spin is confined to the domain walls, slowing down domain wall dynamics via exchange collisions. Later, coherence in the domain wall decreases, and the velocity of the wall increases. We demonstrate that spontaneous domain-wall motion may be tuned through altering the initial domain orientation and coherence in the domain wall and have modeled the observed wall trajectories with numerical solutions of a quantum Boltzmann equation. We also use simulations of the quantum Boltzmann equation to train a neural network to predict initial conditions that lead to specific target domain wall trajectories. Achievable spontaneous domain wall trajectories are limited by the restricted phase space of initial parameters; however, optically applying effective magnetic field gradients alters spin currents through the domain wall and offers the possibility of dynamic control of wall motion. We present progress toward this goal using machine-learning techniques to predict time-varying effective magnetic field gradients as control parameters.

Keyword-1 Spinor gas
Keyword-2 Spin transport
Keyword-3 Out-of-equilibrium Bose gas

Authors

Olha Farion (Simon Fraser University) Mehdi Pourzand (Simon Fraser University) Jeffrey McGuirk (Simon Fraser University)

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