SAMOP 2021 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
A: Fachverband Atomphysik
A 13: Quantum Gases and Matter Waves (joint session Q/A)
A 13.9: Poster
Dienstag, 21. September 2021, 16:30–18:30, P
Unsupervised machine learning of topological phase transitions from experimental data — •Niklas Käming1, Anna Dawid2,3, Korbinian Kottmann3, Maciej Lewenstein3,4, Klaus Sengstock1,5,6, Alexandre Dauphin3, and Christof Weitenberg1,5 — 1Institut für Laserphysik, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany — 2Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland — 3Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss 3, 08860 Castelldefels (Barcelona), Spain — 4ICREA, Pg. Lluís Campanys 23, 08010 Barcelona, Spain — 5The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany — 6Zentrum für Optische Quantentechnologien, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
Identifying phase transitions is one of the key challenges in quantum many-body physics. Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries from noisy and imperfect data without the knowledge of the order parameter. Here, we apply different unsupervised machine learning techniques to experimental data from ultracold atoms. In this way, we obtain the topological phase diagram of the Haldane model in a completely unbiased fashion. We show that these methods can successfully be applied to experimental data at finite temperatures and to the data of Floquet systems when post-processing the data to a single micromotion phase.