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P. Hatfield

Publications and source records attributed to P. Hatfield.

5 recordsLinked to original sources

Bounding elastic photon-photon scattering at $\sqrt s \approx 1\,$MeV using a laser-plasma platform

We report on a direct search for elastic photon-photon scattering using x-ray and $\gamma$ photons from a laser-plasma based experiment. A gamma photon beam produced by a laser wakefield accelerator provided a broadband gamma spectrum extending to above $E_\gamma = 200$ MeV. These were collided with a dense x-ray field produced by the emission from a laser heated germanium foil at $E_x \approx 1.4$ keV, corresponding to an invariant mass of $\sqrt{s} = 1.22 \pm 0.22$ MeV. In these asymmetric collisions elastic scattering removes one x-ray and one high-energy $\gamma$ photon and outputs two lower energy $\gamma$ photons. No changes in the $\gamma$ photon spectrum were observed as a result of the collisions allowing us to place a 95% upper bound on the cross section of $1.5 \times 10^{15}\,\mu$b. Although far from the QED prediction, this represents the lowest upper limit obtained so far for $\sqrt{s} \lesssim 1$ MeV.

hep-ex

A laser-plasma platform for photon-photon physics

We describe a laser-plasma platform for photon-photon collision experiments to measure fundamental quantum electrodynamic processes such as the linear Breit-Wheeler process with real photons. The platform has been developed using the Gemini laser facility at the Rutherford Appleton Laboratory. A laser wakefield accelerator and a bremsstrahlung convertor are used to generate a collimated beam of photons with energies of hundreds of MeV, that collide with keV x-ray photons generated by a laser heated plasma target. To detect the pairs generated by the photon-photon collisions, a magnetic transport system has been developed which directs the pairs onto scintillation-based and hybrid silicon pixel single particle detectors. We present commissioning results from an experimental campaign using this laser-plasma platform for photon-photon physics, demonstrating successful generation of both photon sources, characterisation of the magnetic transport system and calibration of the single particle detectors, and discuss the feasibility of this platform for the observation of the Breit-Wheeler process. The design of the platform will also serve as the basis for the investigation of strong-field quantum electrodynamic processes such as the nonlinear Breit-Wheeler and the Trident process, or eventually, photon-photon scattering.

physics.plasm-ph

Automation and control of laser wakefield accelerators using Bayesian optimisation

Laser wakefield accelerators promise to revolutionise many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimisation of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimised its outputs by simultaneously varying up to 6 parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimisation of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.

physics.acc-ph

Building high accuracy emulators for scientific simulations with deep neural architecture search

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully accelerates simulations by up to 2 billion times in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

stat.ML

The Rich Are Different: Evidence from the RAVE Survey for Stellar Radial Migration

Using the RAdial Velocity Experiment fourth data release (RAVE DR4), and a new metallicity calibration that will be also taken into account in the future RAVE DR5, we investigate the existence and the properties of super-solar metallicity stars ([M/H] > +0.1 dex) in the sample, and in particular in the Solar neighbourhood. We find that RAVE is rich in super-solar metallicity stars, and that the local metallicity distribution function declines remarkably slowly up to +0.4 dex. Our results show that the kinematics and height distributions of the super-solar metallicity stars are identical to those of the [M/H] < 0 thin-disc giants that we presume were locally manufactured. The eccentricities of the super-solar metallicity stars indicate that half of them are on a roughly circular orbit (e < 0.15), so under the assumption that the metallicity of the interstellar medium at a given radius never decreases with time, they must have increased their angular momenta by scattering at corotation resonances of spiral arms from regions far inside the Solar annulus. The likelihood that a star will migrate radially does not seem to decrease significantly with increasing amplitude of vertical oscillations within range of oscillation amplitudes encountered in the disc.

astro-ph.GA