Imaging the collective excitations of an ultracold gas using statistical   correlations
  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. 
Romain Dubessy, Camilla De Rossi, Thomas Badr, Laurent Longchambon,   H\'el\`ene Perrin
quant-ph cond-mat.quant-gas