Machine Laerning-aided analysis of SU(N) fermions (Thermodynamics)Heurisitic machinery for thermodynamic studies of SU(N) fermions with neural networks The power of machine learning (ML) provides the possibility of analyzing experimental measurements with an unprecedented sensitivity. Here, we introduce a heuristic machinery by using machine learning analysis. We use our machinery to guide the thermodynamic studies in the density profile of ultracold fermions interacting within SU(N) spin symmetry prepared in a quantum simulator. Guided by our machinery, we directly measure a thermodynamic compressibility from density fluctuations within the single image. Our machine learning framework shows a potential to validate theoretical descriptions of SU(N) Fermi liquids, and to identify less-pronounced effects even for highly complex quantum matter with minimal prior understanding.:2006:14142 (2020)In Press, arXivSU(N) fermions - BosonizationEvidence for bosonization in a three-dimensional gas of SU(N) Fermions SU(N) fermions - 2DCollective excitations in two-dimensional SU(N) Fermi gases with tunable spin We measure collective excitations of a harmonically trapped two-dimensional (2D) SU(N) Fermi gas of 173Yb confined to a stack of layers formed by a one-dimensional optical lattice. Quadrupole and breathing modes are excited and monitored with tunable spin. We observe that the quadrupole mode frequency decreases with increasing number of spin components due to the amplification of the interaction effect by N. Our result paves the way to investigate the collective property of 2D SU(N) Fermi liquid with enlarged spin.Efficient two-stage slowing for Erbium quantum gases Efficient production of a narrow-line erbium magneto-optical trap with two-stage slowing Topological matter in optical latticesObservation of nodal-line semimetal with ultracold fermions in an optical lattice The observation of topological phases beyond two dimensions, as widely reported in solid-state systems, has been an open challenge for ultracold atoms. Here, we for the first time realize a 3D spin–orbit coupled nodal-line semimetal in an optical Raman lattice filled with ultracold fermions, and observe the bulk line nodes in the band structure. Our results demonstrate an approach to effectively observe 3D band topology, and open the way to probe exotic topological physics for ultracold atoms in high dimensions.Nature Physics 15 911-916 (2019) [Journal] Topological matter in optical latticesObservation of symmetry-protected topological band with ultracold fermions Symmetry plays a fundamental role in understanding complex quantum matter, particularly in classifying topological quantum phases. An outstanding example is the time-reversal invariant topological insulator, a symmetry-protected topological (SPT) phase in the symplectic class of the Altland-Zirnbauer classification. We report the observation for ultracold atoms of a noninteracting SPT band in a one-dimensional optical lattice. This work opens the way to expanding the scope of SPT physics with ultracold atoms and studying nonequilibrium quantum dynamics in these exotic systems.Science Advances 4, eaao4748 (2018) [Journal] Spin-orbit coupling for ultracold fermionsSpin-orbit-coupled two-electron Fermi gases of ytterbium atoms We demonstrate all-optical implementation of spin-orbit coupling (SOC) in a two-electron Fermi gas of 173Yb atoms by coupling two hyperfine ground states with a narrow optical transition. Due to the SU(N) symmetry of the 1S0 ground-state manifold which is insensitive to external magnetic fields, an optical ac Stark effect is applied to split the ground spin states. The realization of all-optical SOC for ytterbium fermions should offer a route to a long-lived spin-orbit-coupled Fermi gas and greatly expand our capability of studying spin-orbit physics with alkaline-earth-metal-like atoms.Phys. Rev. A Rapid Comm. 94, 061604(R) (2016) [Journal] |

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