511 papers
This category explores the fascinating world of quantum gases, where scientists cool atoms to temperatures near absolute zero to create exotic states of matter. In these extreme conditions, individual atoms begin to behave like a single giant wave, revealing strange quantum effects that are usually hidden in our everyday warm world. These experiments help researchers understand the fundamental rules governing matter and could one day lead to revolutionary new technologies like ultra-precise sensors or quantum computers.
On Gist.Science, we process every new preprint in this field directly from arXiv to make these complex discoveries accessible to everyone. Our team provides both plain-language overviews for the curious mind and detailed technical summaries for experts, ensuring you get the full picture without getting lost in the jargon. Below are the latest papers from arXiv in Cond-Mat — Quant-Gas, freshly summarized and ready for you to explore.
This paper reports the successful sympathetic cooling and Coulomb crystallization of xenon highly charged ions within a laser-cooled calcium ion matrix, establishing a versatile platform for precision metrology, new physics searches, and quantum information science.
This brief survey aims to clarify existing controversies and confusions in the literature regarding Bose-Einstein condensation by elucidating the relationships between key concepts such as spontaneous gauge symmetry breaking, ergodic decomposition, particle fluctuations, and system stability.
Motivated by recent experimental observations of dipolar supersolid stripes, this paper employs Bogoliubov theory to characterize the zero- and finite-temperature physics of strongly dipolar, fully polarized dipoles in a quasi-2D trapped geometry, revealing how tilting angle, particle number, scattering length, and trap aspect ratio influence spatial modulations and liquid character, including a notable temperature-induced promotion of spatial structures.
This paper demonstrates the first three-dimensional magneto-optical trapping of the metal hydride molecule CaH, achieving temperatures below one millikelvin and opening a pathway for the optical trapping of hydrogen atoms via controlled molecular dissociation.
This paper establishes a unified framework demonstrating that the critical behavior of ideal Bose-Einstein condensation falls into three distinct classes determined solely by the low-energy scaling of the density of states, which is governed by dimensionality and confinement.
This paper investigates the dynamics of repeated Bose-Einstein condensate formation and extraction in a dimple trap using a kinetic model, demonstrating that partial extraction protocols combined with continuous thermal-atom replenishment maximize production efficiency by leveraging residual atoms to seed subsequent condensate growth while balancing density-dependent losses.
This paper models the tidal disruption of a cold helium white dwarf by a black hole as a Bose-Fermi droplet, predicting that the resulting accretion disc exhibits quantized vortices causing characteristic electromagnetic flickering, while vortices on the escaping white dwarf induce rotation and gravitational wave emission.
Using the cluster Gutzwiller mean-field method, this study constructs the first grand-canonical phase diagram for repulsive bosons on a two-leg ladder with artificial magnetic flux, revealing how flux modifies Mott lobe structures and demonstrating that a combined symmetry at flux suppresses chiral currents to produce a nonchiral Mott-insulating state.
This paper derives a first-order perturbative recursion formula for the canonical partition function of weakly interacting Bose gases, demonstrating that while it shares the same Feynman diagrams as the grand-canonical approach, it employs distinct rules to accurately characterize ground-state occupancy statistics and thermodynamic properties in box traps with Dirichlet boundary conditions.
This paper presents a highly scalable, data-driven framework that combines gradient-based machine learning optimization with tensor network representations to efficiently learn interacting many-body Hamiltonians from limited dynamical data, demonstrating robust performance for systems exceeding 100 spins even with restricted initial states, observables, and short time evolutions.