Ultracold quantum matter

Abstract:

We present the first demonstration of velocity-selective pumping in an atomic vapour to preserve light-matter coherence. Control is illustrated by a subsequent demonstration of an atomic frequency comb quantum memory realised in the vapour.

Abstract:

© 2020, The Author(s). We propose in this White Paper a concept for a space experiment using cold atoms to search for ultra-light dark matter, and to detect gravitational waves in the frequency range between the most sensitive ranges of LISA and the terrestrial LIGO/Virgo/KAGRA/INDIGO experiments. This interdisciplinary experiment, called Atomic Experiment for Dark Matter and Gravity Exploration (AEDGE), will also complement other planned searches for dark matter, and exploit synergies with other gravitational wave detectors. We give examples of the extended range of sensitivity to ultra-light dark matter offered by AEDGE, and how its gravitational-wave measurements could explore the assembly of super-massive black holes, first-order phase transitions in the early universe and cosmic strings. AEDGE will be based upon technologies now being developed for terrestrial experiments using cold atoms, and will benefit from the space experience obtained with, e.g., LISA and cold atom experiments in microgravity. KCL-PH-TH/2019-65, CERN-TH-2019-126.

Abstract:

We apply three machine learning strategies to optimize the atomic cooling processes utilized in the production of a Bose–Einstein condensate (BEC). For the first time, we optimize both laser cooling and evaporative cooling mechanisms simultaneously. We present the results of an evolutionary optimization method (differential evolution), a method based on non-parametric inference (Gaussian process regression) and a gradient-based function approximator (artificial neural network). Online optimization is performed using no prior knowledge of the apparatus, and the learner succeeds in creating a BEC from completely randomized initial parameters. Optimizing these cooling processes results in a factor of four increase in BEC atom number compared to our manually-optimized parameters. This automated approach can maintain close-to-optimal performance in long-term operation. Furthermore, we show that machine learning techniques can be used to identify the main sources of instability within the apparatus.

Abstract:

© 2020 Elsevier B.V. The (py)LIon package is a set of tools to simulate the classical trajectories of ensembles of ions in electrodynamic traps. Molecular dynamics simulations are performed using LAMMPS, an efficient and feature-rich program. (py)LIon has been validated by comparison with the analytic theory describing ion trap dynamics. Notable features include GPU-accelerated force calculations, and treating collections of ions as rigid bodies to enable investigations of the rotational dynamics of large, mesoscopic charged particles. Programme summary: Program Title: (py)LIon Program Files doi: http://dx.doi.org/10.17632/ywwd9nnxjh.1 Licencing provisions: MIT Programming language: Matlab, Python Subprograms used: LAMMPS Nature of problem: Simulating the dynamics of ions and mesoscopic charged particles confined in an electrodynamic trap using molecular dynamics methods Solution method: Provide a tested, feature-rich API to configure molecular dynamics calculations in LAMMPS Unusual features: (py)LIon can treat collections of ions as rigid bodies to simulate larger objects confined in electrodynamic traps. GPU acceleration is provided through the LAMMPS [Formula presented] package.