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In situ magnetometry for experiments with atomic quantum gases.

Ludwig Krinner1, Michael Stewart1, Arturo Pazmiño1

  • 1Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794-3800, USA.

The Review of Scientific Instruments
|February 3, 2018
PubMed
Summary

This study introduces a simple in situ method for monitoring magnetic fields in atomic quantum gas experiments. The technique achieves high accuracy and millisecond resolution, aiding experiments where field stabilization is difficult.

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Area of Science:

  • Atomic, Molecular, and Optical Physics
  • Quantum Gases
  • Magnetometry

Background:

  • Precise magnetic field control is crucial for atomic quantum gas experiments.
  • Dedicated field stabilization is not always feasible in experimental setups.
  • Existing methods may lack in situ monitoring capabilities or require specific experimental conditions.

Purpose of the Study:

  • To present a simple, in situ method for monitoring magnetic fields in quantum gas experiments.
  • To provide a readily implementable solution for apparatuses lacking dedicated field stabilization.
  • To demonstrate the method's applicability across various experimental conditions and fields.

Main Methods:

  • Sampling Rabi resonances between magnetically field-sensitive internal atomic states.
  • Utilizing internal states not actively used in the primary experiment.
  • Integrating the monitoring sequence with standard measurement protocols.

Main Results:

  • Demonstrated reconstruction of Gauss-level bias fields for 87Rb condensates.
  • Achieved high accuracy (tens of microgauss) and millisecond time resolution.
  • Validated performance using slow resonant Rabi oscillations and demonstrated application with optical potentials.

Conclusions:

  • The presented method offers a practical solution for in situ magnetic field monitoring in diverse quantum gas experiments.
  • It enhances experimental precision and control without requiring dedicated stabilization hardware.
  • The technique is versatile and can be integrated into existing experimental workflows.