Abstract

Motivated by recent experiments and theoretical investigations on binary mixtures, we investigate the miscible-immiscible transition at finite temperature by means of Quantum Monte Carlo. Based on the observation that the segregated phase is strongly affected by temperature, we propose to use the degree of demixing for thermometry of a binary bosonic mixture trapped in an optical lattice. We show that the proposed method is especially sensitive at low temperatures, of the order of the tunnelling amplitude, and therefore is particularly suitable in the regime where quantum magnetism is expected.

Highlights

  • Motivated by recent experiments and theoretical investigations on binary mixtures, we investigate the miscible-immiscible transition at finite temperature by means of Quantum Monte Carlo

  • The physical reason is that the primary thermometric quantity used in cold atoms experiments, namely the momentum distribution, in strongly correlated regimes is often dominated by quantum rather than thermal fluctuations, thereby becoming quite insensitive to temperature variations

  • We propose a thermometry technique for ultracold quantum mixtures in optical lattices based on the demixing of two mutually repulsive components

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Summary

Introduction

Motivated by recent experiments and theoretical investigations on binary mixtures, we investigate the miscible-immiscible transition at finite temperature by means of Quantum Monte Carlo. Based on the observation that the segregated phase is strongly affected by temperature, we propose to use the degree of demixing for thermometry of a binary bosonic mixture trapped in an optical lattice. We propose a thermometry technique for ultracold quantum mixtures in optical lattices based on the demixing of two mutually repulsive components. In the case of a deeper optical lattice, a strongly-interacting (SI) regime[31], we lack reliable temperature estimators other than the direct microscopic observation of particle-hole pairs. In this regime, our proposed thermometry proves a remarkably effective tool. We show how the degree of demixing characterizing the spatial www.nature.com/scientificreports/

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