Imaging the collective excitations of an ultracold gas using statistical correlations
arXiv:1410.1675 · doi:10.1088/1367-2630/16/12/122001
Abstract
Advanced data analysis techniques have proved to be crucial for extracting information from noisy images. Here we show that principal component analysis can be successfully applied to ultracold gases to unveil their collective excitations. By analyzing the correlations in a series of images we are able to identify the collective modes which are excited, determine their population, image their eigenfunction, and measure their frequency. Our method allows to discriminate the relevant modes from other noise components and is robust with respect to the data sampling procedure. It can be extended to other dynamical systems including cavity polariton quantum gases or trapped ions.
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- Trapping atoms with radio-frequency adiabatic potentials
- Low-energy collective oscillations and Bogoliubov sound in an exciton-polariton condensate
- Effective statistical fringe removal algorithm for high-sensitivity imaging of ultracold atoms
- Decoupled sound and amplitude modes in trapped dipolar supersolids
- Multi-frequency optical lattice for dynamic lattice-geometry control
- Reconstructing 2D spatial modes for classical and quantum light
- Robust inertial sensing with point-source atom interferometry for interferograms spanning a partial period
- Study to improve the performance of interferometer with ultra-cold atoms