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20242026
most citedAnomalous minimization for critical velocity of superflow along a step potential

1 citations · 1 across the 2 of their papers we have counts for

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7 papers

cond-mat.quant-gas2026

Effect of Population Imbalance on Vortex Mass in Superfluid Fermi Gases

Lucas Levrouw, Hiromitsu Takeuchi, Jacques Tempere

One of the fundamental parameters associated with quantized vortices in superfluids is the vortex mass, which is the inertia of a vortex. As of yet, this mass has not been observed…

cond-mat.quant-gas20261 cited

Anomalous minimization for critical velocity of superflow along a step potential

Akihiro Kanjo, Hiromitsu Takeuchi

To reveal a microscopic mechanism for the anomalous minimization and dependence of the superfluid critical velocity on a moving obstacle potential in a atomic Bose-Einstein condens…

cond-mat.quant-gas2025

Vortex Mass in Superfluid Fermi Gases along the BEC-BCS Crossover

Lucas Levrouw, Hiromitsu Takeuchi, Jacques Tempere

Vortex mass is a key concept in the study of superfluid dynamics, referring to the inertia of vortices in a superfluid, which affects their motion and behavior. Despite being an im…

cond-mat.quant-gas2025

Stable singular fractional skyrmion spin texture from the quantum Kelvin-Helmholtz instability

SeungJung Huh, Wooyoung Yun, Gabin Yun +7

Topology profoundly influences diverse fields of science, providing a powerful framework for classifying phases of matter and predicting nontrivial excitations, such as solitons, v…

cond-mat.quant-gas2025

Dynamic scaling of vorticity in phase-separating superfluid mixtures

Ryuta Ito, Hiromitsu Takeuchi

Recently, it has been experimentally confirmed that non-equilibrium dynamics of phase separation in strongly ferromagnetic Bose-Einstein condensates of Li atoms obey the dynami…

cond-mat.quant-gas2025

Proper Orthogonal Decomposition of a Superfluid Turbulent Wake

Sota Yoneda, Hiromitsu Takeuchi

Superfluid turbulent wakes behind a square prism are studied theoretically and numerically by proper orthogonal decomposition (POD). POD is a data science approach that can efficie…