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Dielectric axion haloscopes, such as the MADMAX experiment, are promising concepts for the direct search for dark matter axions. A reliable simulation is a fundamental requirement for the successful realisation of the experiments. Due to the complexity of the simulations, the demands on computing resources can quickly become prohibitive. In this paper, we show for the first time that modern deep learning techniques can be applied to aid the simulation and optimisation of dielectric haloscopes., Comment: 7 pages, 6 figures

The AWAKE experiment requires an automated online rubidium (Rb) plasma density and gradient diagnostic for densities between 1 and 10$\cdot$10$^{14}$ cm$^{-3}$. A linear density gradient along the plasma source at the percent level may be useful to improve the electron acceleration process. Because of full laser ionization of Rb vapor to Rb$^{+}$ within a radius of 1 mm, the plasma density equals the vapor density. We measure the Rb vapor densities at both ends of the source, with high precision using, white light interferometry. At either source end, broadband laser light passes a remotely controlled Mach-Zehnder interferometer built out of single mode fibers. The resulting interference signal, influenced by dispersion in the vicinity of the Rb D1 and D2 transitions, is dispersed in wavelength by a spectrograph. Fully automated Fourier-based signal conditioning and a fit algorithm yield the density with an uncertainty between the measurements at both ends of 0.11 to 0.46 $\%$ over the entire density range. These densities used to operate the plasma source are displayed live in the control room., Comment: 5 pages, 8 figures, EAAC2017 conference proceeding