Optimized observable readout from single-shot images of ultracold atoms via machine learning

Journal article

Single-shot images are the standard readout of experiments with ultracold atoms, the imperfect reflection of their many-body physics. The efficient extraction of observables from single-shot images is thus crucial. Here we demonstrate how artificial neural networks can optimize this extraction. In contrast to standard averaging approaches, machine learning allows both one- and two-particle densities to be accurately obtained from a drastically reduced number of single-shot images. Quantum fluctuations and correlations are directly harnessed to obtain physical observables for bosons in a tilted double-well potential at an extreme accuracy. Strikingly, machine learning also enables a reliable extraction of momentum-space observables from real-space single-shot images and vice versa. With this technique, the reconfiguration of the experimental setup between in situ and time-of-flight imaging is required only once to obtain training data, thus potentially granting an outstanding reduction in resources.

Authors: Lode, AUJ; Lin, R; Buettner, M; Papariello, L; Leveque, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Physical Society
• Journal: Physical Review A
• Volume: 104
• Issue: 4
• Article number: L041301
• Publication date: 2021-10-08
• Acceptance date: 2021-09-08
• DOI: 10.1103/PhysRevA.104.L041301
• EISSN: 2469-9934
• ISSN: 2469-9926
• Language: English
• Keywords: FFR