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Investigating out-of-equilibrium dynamics with two-dimensional (2D) systems is of widespread theoretical interest, as these systems are strongly influenced by fluctuations and there exists a superfluid phase transition at a finite temperature. In this work, we realise matter-wave interference for degenerate Bose gases, including the first demonstration of coherent splitting of 2D Bose gases using magnetic trapping potentials. We improve the fringe contrast by imaging only a thin slice of the expanded atom clouds, which will be necessary for subsequent studies on the relaxation of the gas following a quantum quench.

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.

We dress atoms with multiple-radiofrequency (RF) fields and investigate the spectrum of transitions driven by an additional probe field. A complete theoretical description of this rich spectrum is presented, in which we find allowed transitions and determine their amplitudes using the resolvent formalism. Experimentally, we observe transitions up to sixth order in the probe field using RF spectroscopy of Bose-Einstein condensates trapped in single- and multiple-RF-dressed potentials. We find excellent agreement between theory and experiment, including the prediction and verification of previously unobserved transitions, even in the single-RF case.

Quantum vortices play a crucial role in both equilibrium and dynamical phenomena in two-dimensional (2D) superfluid systems. Experimental detection of these excitations in 2D ultracold atomic gases typically involves examining density depletions in absorption images, however the presence of a significant phononic background renders the problem challenging, beyond the capability of simple algorithms or the human eye. Here, we utilize a convolutional neural network to detect vortices in the presence of strong long- and intermediate-length scale density modulations in finite-temperature 2D Bose gases. We train the model on datasets obtained from ab initio Monte Carlo simulations using the classical-field method for density and phase fluctuations, and Gross–Pitaevskii simulation of realistic expansion dynamics. We use the model to analyze experimental images and benchmark its performance by comparing the results to the matter-wave interferometric detection of vortices, confirming the observed scaling of vortex density across the Berezinskii–Kosterlitz–Thouless critical point. The combination of a relevant simulation pipeline with machine-learning methods is a key development towards the comprehensive understanding of complex vortex-phonon dynamics in out-of-equilibrium 2D quantum systems.

We report on developments in the atomic fountain systems being built and operated at NPL. An improved generation of Cs fountains has been developed, with units being constructed for use by both NPL and commercial customers. These systems combine world-class stability and accuracy with increased reliability and can run for long periods of time without maintenance. Here we describe how the NPL fountains work, and present performance data for the latest systems. We describe some of the applications of these fountains, both in time scale implementation and fundamental science. We also present an overview of a miniature atomic fountain that is being developed, which will help make fountain technology accessible to a wider range of sectors.

This book is primarily intended to accompany an advanced undergraduate course in atomic physics. However, the elementary atomic physics covered in the early chapters should be accessible to undergraduates when they are first introduced to the subject. The book describes some of the latest advances and the applications to Bose-Einstein condensation of atoms, matter-wave interferometry and quantum computing with trapped ions. To complement the usual quantum mechanical treatment of atomic structure the book strongly emphasizes the experimental basis of the subject, especially in the later chapters.

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.

We describe the low-energy scattering theory relevant to the description of the Bose-Einstein condensed gases recently produced using evaporative cooling. We examine the validity range of the approximations being used to describe the ultracold interactions in the context of the interaction between caesium atoms at the temperatures produced by evaporation in a magnetic trap. We discuss the prospects for future developments in the field.

Techniques to manipulate the individual constituents of an ultracold mixture are key to investigating impurity physics. In this work, we confine a mixture of the hyperfine ground states of Rb-87 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 individually controlled to independently manipulate each species. Furthermore, we verify that our mixture of hyperfine states is collisionally stable, with no observable inelastic loss.

We describe the operation of an electrodynamic ion trap in which the electric quadrupole field oscillates at two frequencies. This mode of operation allows simultaneous tight confinement of ions with extremely different charge-to-mass ratios, e.g., singly ionised atomic ions together with multiply charged nanoparticles. We derive the stability conditions for two-frequency operation from asymptotic properties of the solutions of the Mathieu equation and give a general treatment of the effect of damping on parametric resonances. Two-frequency operation is effective when the two species' mass ratios and charge ratios are sufficiently large, and further when the frequencies required to optimally trap each species are widely separated. This system resembles two coincident Paul traps, each operating close to a frequency optimized for one of the species, such that both species are tightly confined. This method of operation provides an advantage over single-frequency Paul traps, in which the more weakly confined species forms a sheath around a central core of tightly confined ions. We verify these ideas using numerical simulations and by measuring the parametric heating induced in experiments by the additional driving frequency.