Itinerant ferromagnetism in an interacting Fermi gas with mass imbalance

We study the emergence of itinerant ferromagnetism in an ultra-cold atomic gas with a variable mass ratio between the up and down spin species. Mass imbalance breaks the SU(2) spin symmetry leading to a modified Stoner criterion. We first elucidate the phase behavior in both the grand canonical and canonical ensembles. Secondly, we apply the formalism to a harmonic trap to demonstrate how a mass imbalance delivers unique experimental signatures of ferromagnetism. These could help future experiments to better identify the putative ferromagnetic state. Furthermore, we highlight how a mass imbalance suppresses the three-body loss processes that handicap the formation of a ferromagnetic state. Finally, we study the time dependent formation of the ferromagnetic phase following a quench in the interaction strength

Itinerant ferromagnetism in a two-dimensional atomic gas

Motivated by the first experimental evidence of ferromagnetic behavior in a three-dimensional ultracold atomic gas, we explore the possibility of itinerant ferromagnetism in a trapped two-dimensional atomic gas. Firstly, we develop a formalism that demonstrates how quantum fluctuations drive the ferromagnetic reconstruction first order, and consider the consequences of an imposed population imbalance. Secondly, we adapt this formalism to elucidate the key experimental signatures of ferromagnetism in a realistic trapped geometry.Comment: Accepted for publication in Phys. Rev.

Predicting the operability of damaged compressors using machine learning

Abstract The application of machine learning to aerospace problems faces a particular challenge. For successful learning a large amount of good quality training data is required, typically tens of thousands of cases. However, due to the time and cost of experimental aerospace testing, this data is scarce. This paper shows that successful learning is possible with two novel techniques: The first technique is rapid testing. Over the last five years the Whittle Laboratory has developed a capability where rebuild and test times of a compressor stage now take 15 minutes instead of weeks. The second technique is to base machine learning on physical parameters, derived from engineering wisdom developed in industry over many decades. The method is applied to the important industry problem of predicting the effect of blade damage on compressor operability. The current approach has high uncertainty, it is based on human judgement and correlation of a handful of experimental test cases. It is shown using 100 training cases and 25 test cases that the new method is able to predict the operability of damaged compressor stages with an accuracy of 2% in a 95% confidence interval; far better than is possible by even the most experienced compressor designers. Use of the method is also shown to generate new physical understanding, previously unknown by any of the experts involved in this work. Using this method in the future offers an exciting opportunity to generate understanding of previously intractable problems in aerospace.Aerospace Technology Institute Rolls-Royce plc

Accelerating the Design of Automotive Catalyst Products Using Machine Learning Leveraging experimental data to guide new formulations

The design of catalyst products to reduce harmful emissions is currently an intensive process of expert-driven discovery, taking several years to develop a product. Machine learning can accelerate this timescale, leveraging historic experimental data from related products to guide which new formulations and experiments will enable a project to most directly reach its targets. We used machine learning to accurately model 16 key performance targets for catalyst products, enabling detailed understanding of the factors governing catalyst performance and realistic suggestions of future experiments to rapidly develop more effective products. The proposed formulations are currently undergoing experimental validation.</jats:p

Many-flavor electron gas approach to electron-hole drops

A many-flavor electron gas (MFEG) is analyzed, such as could be found in a multi-valley semiconductor or semimetal. Using the re-derived polarizability for the MFEG an exact expression for the total energy of a uniform MFEG in the many-flavor approximation is found; the interacting energy per particle is shown to be -0.574447E_h a_0^3/4 m*^3/4 n^1/4 with E_h being the Hartree energy, a_0 Bohr radius, and m^* particle effective mass. The short characteristic length-scale of the MFEG motivates a local density approximation, allowing a gradient expansion in the energy density, and the expansion scheme is applied to electron-hole drops, finding a new form for the density profile and its surface scaling properties.Comment: 11 pages, 5 figure

Quantum condensation in electron-hole bilayers with density imbalance

We study the two-dimensional spatially separated electron-hole system with density imbalance at absolute zero temperature. By means of the mean-field theory, we find that the Fulde-Ferrell state is fairly stabilized by the order parameter mixing effect.Comment: 5 pages, 5 figure