Realising a species-selective double well with multiple-radiofrequency-dressed potentials
Journal of Physics B: Atomic, Molecular and Optical Physics IOP Publishing 53:15 (2020) 155001
Abstract:
Techniques to manipulate the individual constituents of an ultracold mixture are key to investigating impurity physics. In this work, we confine a mixture of hyperfine ground states of 87Rb atoms in a double-well potential. The potential is produced by dressing the atoms with multiple radiofrequencies. The amplitude and phase of each frequency component of the dressing field are controlled to independently manipulate each species. Furthermore, we verify that our mixture of hyperfine states is collisionally stable, with no observable inelastic loss.AEDGE: Atomic Experiment for Dark Matter and Gravity Exploration in Space
EPJ QUANTUM TECHNOLOGY Springer Science and Business Media LLC 7:1 (2020) ARTN 6
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.Applying machine learning optimization methods to the production of a quantum gas
Machine Learning: Science and Technology IOP Publishing 1:1 (2020) 015007
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.(py)LIon: A package for simulating trapped ion trajectories
Computer Physics Communications (2020)
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.Probing multiple-frequency atom-photon interactions with ultracold atoms
New Journal of Physics IOP Publishing 21:5 (2019) 073067