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Updated: Jun 14, 2025

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
Published on: March 30, 2017
Collective Dynamical Fermi Suppression of Optically Induced Inelastic Scattering
Camen A Royse1, J Huang1, J E Thomas1
1Department of Physics, <a href="https://ror.org/04tj63d06">North Carolina State University</a>, Raleigh, North Carolina 27695, USA.
We discovered that increasing the s-wave scattering length in a Fermi gas suppresses optical loss. This occurs as the gas enters a magnetized state, where atomic properties limit interactions, enabling optical control of interactions.
Area of Science:
- Atomic, Molecular, and Optical Physics
- Quantum Gases
- Condensed Matter Physics
Background:
- Optically induced loss is a common challenge in ultracold atomic gases.
- Controlling interactions in quantum systems is crucial for quantum technologies.
- Fermi gases offer a platform to study quantum many-body phenomena.
Purpose of the Study:
- To investigate the dynamical suppression of optically induced loss in a Fermi gas.
- To explore the role of interactions and magnetic properties in loss suppression.
- To demonstrate optical control over effective long-range interactions.
Main Methods:
- Utilizing a trapped cigar-shaped Fermi gas.
- Tuning the s-wave scattering length to modify interactions.
- Employing optical control techniques.
- Developing a quasiclassical collective spin vector model incorporating spin-dependent loss.
Main Results:
- Observed strong dynamical suppression of optically induced loss with increasing s-wave scattering length.
- Demonstrated that the Fermi gas acts as a tunable Heisenberg spin lattice.
- Showed loss suppression correlates with the transition to a magnetized state.
- Confirmed that fermionic nature inhibits interactions in the magnetized state.
Conclusions:
- The study successfully demonstrates dynamical loss suppression in Fermi gases via optical control.
- The findings enable the application of optical control for effective long-range interactions.
- The developed model quantitatively explains the observed phenomena, validating the approach.
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