J
Jörg Schmiedmayer
Researcher at Vienna University of Technology
Publications - 358
Citations - 21391
Jörg Schmiedmayer is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Quantum & Ultracold atom. The author has an hindex of 72, co-authored 344 publications receiving 19122 citations. Previous affiliations of Jörg Schmiedmayer include Rowland Institute for Science & University of Innsbruck.
Papers
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Proceedings ArticleDOI
Guides for atoms
TL;DR: In this paper, the evanescent wave was used as a mirror to reflect atoms at normal incidence, which was shown to provide a strong intensity gradient and thus combined a large potential with a very short interaction time, thereby limiting spontaneous emission even further.
Journal ArticleDOI
Bose-Einstein-Kondensat als Magnetfeldsensor
TL;DR: In this article, a new Methode vereint hohe raumliche Auflosung von 3 μm with einer sehr guten Sensitivitat im Bereich von Nanotesla.
Journal ArticleDOI
Extracting the field theory description of a quantum many-body system from experimental data
Torsten V. Zache,Thomas Schweigler,Sebastian Erne,Sebastian Erne,Jörg Schmiedmayer,Jürgen Berges +5 more
TL;DR: In this paper, the authors developed a general pathway to extract the irreducible building blocks of quantum field theoretical descriptions and its parameters from experimental data, which is accomplished by extracting the one-particle IR vertices from which one can construct all observables.
Book ChapterDOI
Matter Wave Diffraction at Standing Light Waves
TL;DR: In this article, the authors show that by increasing the light intensity one can realize higher potentials and reach the regime of channeling, where now the atoms behave like particles and their propagation can be described with classical trajectories.
Journal ArticleDOI
Thermometry of one-dimensional Bose gases with neural networks
Frederik Skovbo Møller,Thomas Schweigler,Thomas Schweigler,Mohammadamin Tajik,Jo{ã}o Sabino,Jo{ã}o Sabino,Federica Cataldini,Si-Cong Ji,Jörg Schmiedmayer +8 more
TL;DR: In this paper, a neural network is trained to predict both the temperature of single realizations of the system and the uncertainty thereof, which can be combined in an estimate of the mean temperature, improving precision.