Positronium S state spectrum: analytic results at O(m alpha^6)

We present an analytic calculation of the O(m alpha^6) recoil and radiative recoil corrections to energy levels of positronium nS states and their hyperfine splitting. A complete analytic formula valid to O(m alpha^6) is given for the spectrum of S states. Technical aspects of the calculation are discussed in detail. Theoretical predictions are given for various energy intervals and compared with experimental results.Comment: 29 pages, revte

A Bayesian method for material identification of composite plates via dispersion curves

Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully defined relationship between central frequency and propagation characteristics (phase velocity, group velocity and wavenumber)—which is described using dispersion curves. For many guided wave-based strategies, accurate dispersion curve information is invaluable, such as group velocity for localisation. From experimental observations of dispersion curves, a system identification procedure can be used to determine the governing material properties. As well as returning an estimated value, it is useful to determine the distribution of these properties based on measured data. A method of simulating samples from these distributions is to use the iterative Markov-Chain Monte Carlo (MCMC) procedure, which allows for freedom in the shape of the posterior. In this work, a scanning-laser Doppler vibrometer is used to record the propagation of Lamb waves in a unidirectional-glass-fibre composite plate, and dispersion curve data for various propagation angles are extracted. Using these measured dispersion curve data, the MCMC sampling procedure is performed to provide a Bayesian approach to determining the dispersion curve information for an arbitrary plate. The distribution of the material properties at each angle is discussed, including the inferred confidence in the predicted parameters. The percentage errors of the estimated values for the parameters were 10–15 points larger when using the most likely estimates, as opposed to calculating from the posterior distributions, highlighting the advantages of using a probabilistic approach

Structured machine learning tools for modelling characteristics of guided waves

The use of ultrasonic guided waves to probe the materials/structures for damage continues to increase in popularity for non-destructive evaluation (NDE) and structural health monitoring (SHM). The use of high-frequency waves such as these offers an advantage over low-frequency methods from their ability to detect damage on a smaller scale. However, in order to assess damage in a structure, and implement any NDE or SHM tool, knowledge of the behaviour of a guided wave throughout the material/structure is important (especially when designing sensor placement for SHM systems). Determining this behaviour is extremely difficult in complex materials, such as fibre–matrix composites, where unique phenomena such as continuous mode conversion takes place. This paper introduces a novel method for modelling the feature-space of guided waves in a composite material. This technique is based on a data-driven model, where prior physical knowledge can be used to create structured machine learning tools; where constraints are applied to provide said structure. The method shown makes use of Gaussian processes, a full Bayesian analysis tool, and in this paper it is shown how physical knowledge of the guided waves can be utilised in modelling using an ML tool. This paper shows that through careful consideration when applying machine learning techniques, more robust models can be generated which offer advantages such as extrapolation ability and physical interpretation

Establishing non-thermal regimes in pump-probe electron-relaxation dynamics

Time- and angle-resolved photoemission spectroscopy (TR-ARPES) accesses the electronic structure of solids under optical excitation, and is a powerful technique for studying the coupling between electrons and collective modes. One approach to infer electron-boson coupling is through the relaxation dynamics of optically-excited electrons, and the characteristic timescales of energy redistribution. A common description of electron relaxation dynamics is through the effective electronic temperature. Such a description requires that thermodynamic quantities are well-defined, an assumption that is generally violated at early delays. Additionally, precise estimation of the non-thermal window -- within which effective temperature models may not be applied -- is challenging. We perform TR-ARPES on graphite and show that Boltzmann rate equations can be used to calculate the time-dependent electronic occupation function, and reproduce experimental features given by non-thermal electron occupation. Using this model, we define a quantitative measure of non-thermal electron occupation and use it to define distinct phases of electron relaxation in the fluence-delay phase space. More generally, this approach can be used to inform the non-thermal-to-thermal crossover in pump-probe experiments.Comment: 18 pages, 10 figure

Direct determination of mode-projected electron-phonon coupling in the time-domain

Ultrafast spectroscopies have become an important tool for elucidating the microscopic description and dynamical properties of quantum materials. In particular, by tracking the dynamics of non-thermal electrons, a material's dominant scattering processes -- and thus the many-body interactions between electrons and collective excitations -- can be revealed. Here we present a new method for extracting the electron-phonon coupling strength in the time domain, by means of time and angle-resolved photoemission spectroscopy (TR-ARPES). This method is demonstrated in graphite, where we investigate the dynamics of photo-injected electrons at the K point, detecting quantized energy-loss processes that correspond to the emission of strongly-coupled optical phonons. We show that the observed characteristic timescale for spectral-weight-transfer mediated by phonon-scattering processes allows for the direct quantitative extraction of electron-phonon matrix elements, for specific modes, and with unprecedented sensitivity.Comment: 19 pages, 4 figure

Thyroid function before, during and after COVID-19

Context: The effects of COVID-19 on the thyroid axis remain uncertain. Recent evidence has been conflicting, with both thyrotoxicosis and suppression of thyroid function reported. Objective: We aimed to detail the acute effects of COVID-19 on thyroid function and determine if these effects persisted upon recovery from COVID-19. Design: Cohort observational study. Participants and setting: Adult patients admitted to Imperial College Healthcare National Health Service Trust, London, UK with suspected COVID-19 between March 9 to April 22, 2020 were included, excluding those with pre-existing thyroid disease and those missing either free thyroxine (FT4) or TSH measurements. Of 456 patients, 334 had COVID-19 and 122 did not. Main Outcome Measures: TSH and FT4 measurements at admission, and where available, those taken in 2019 and at COVID-19 follow-up. Results: Most patients (86·6%) presenting with COVID-19 were euthyroid, with none presenting with overt thyrotoxicosis. Patients with COVID-19 had a lower admission TSH and FT4 compared to those without COVID-19. In the COVID-19 patients with matching baseline thyroid function tests from 2019 (n=185 for TSH and 104 for FT4), both TSH and FT4 were reduced at admission compared to baseline. In a complete cases analysis of COVID-19 patients with TSH measurements at follow-up, admission and baseline (n=55), TSH was seen to recover to baseline at follow-up. Conclusions: Most patients with COVID-19 present with euthyroidism. We observed mild reductions in TSH and FT4 in keeping with a non-thyroidal illness syndrome. Furthermore, in survivors of COVID-19, thyroid function tests at follow-up returned to baseline

On the Transfer of Damage Detectors Between Structures: An Experimental Case Study

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordIncomplete data – which fail to represent environmental effects or damage – are a significant challenge for structural health monitoring (SHM). Population-based frameworks offer one solution by considering that information might be shared, in some sense, between similar structures. In this work, the data from a group of aircraft tailplanes are considered collectively, in a shared (more consistent) latent space. As a result, the measurements from one tailplane enable damage detection in another, utilising various pair-wise comparisons within the population. Specifically, Transfer Component Analysis (TCA) is applied to match the normal condition data from different population members. The resulting nonlinear projection leads to a general representation for the normal condition across the population, which informs damage detection via measures of discordancy. The method is applied to a experimental dataset, based on vibration-based laser vibrometer measurements from three tailplanes. By considering the partial datasets together, consistent damage-sensitive features can be defined, leading to an 87% increase in the true positive rate, compared to conventional SHM.Engineering and Physical Sciences Research Council (EPSRC

A Bayesian method for material identification of composite plates via dispersion curves

Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully-defined relationship between central frequency and propagation characteristics (phase velocity, group velocity and wavenumber) -- which is described using dispersion curves. For many guided wave-based strategies, accurate dispersion curve information is invaluable, such as group velocity for localisation. From experimental observations of dispersion curves, a system identification procedure can be used to determine the governing material properties. As well as returning an estimated value, it is useful to determine the distribution of these properties based on measured data. A method of simulating samples from these distributions is to use the iterative Markov-Chain Monte Carlo procedure, which allows for freedom in the shape of the posterior. In this work, a scanning-laser doppler vibrometer is used to record the propagation of Lamb waves in a unidirectional-glass-fibre composite plate, and dispersion curve data for various propagation angles are extracted. Using these measured dispersion curve data, the MCMC sampling procedure is performed to provide a Bayesian approach to determining the dispersion curve information for an arbitrary plate