Machine Learning the Phase Diagram of a Strongly Interacting Fermi Gas.

We determine the phase diagram of strongly correlated fermions in the crossover from Bose-Einstein condensates of molecules (BEC) to Cooper pairs of fermions (BCS) utilizing an artificial neural network. By applying advanced image recognition techniques to the momentum distribution of the fermions, a quantity which has been widely considered as featureless for providing information about the condensed state, we measure the critical temperature and show that it exhibits a maximum on the bosonic side of the crossover. Additionally, we backanalyze the trained neural network and demonstrate that it interprets physically relevant quantities.

Phase noise cancellation in polarisation-maintaining fibre links.

The distribution of ultra-narrow linewidth laser radiation is an integral part of many challenging metrological applications. Changes in the optical pathlength induced by environmental disturbances compromise the stability and accuracy of optical fibre networks distributing the laser light and call for active phase noise cancellation. Here we present a laboratory scale optical (at 578 nm) fibre network featuring all polarisation maintaining fibres in a setup with low optical powers available and tracking voltage-controlled oscillators implemented. The stability and accuracy of this system reach performance levels below 1 × 10-19 after 10 000 s of averaging.