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Tessa Charles

Publications and source records attributed to Tessa Charles.

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Arc and Chicane Bunch Compression Schemes for Hard and Soft X-Ray Free Electron Laser Facilities: A Comparison

X-ray free-electron laser (XFEL) facilities require progressive compression of electron bunches as they are accelerated from an injector to the undulators. This is necessary to achieve the peak currents required for efficient lasing, without compromising transverse brightness. In the present generation of XFELs, high peak currents are achieved by means of a sequence of four-dipole bunch compression chicanes. It is well known that these systems are not ideal in that they allow projected emittance dilution at the percent level, and they exhibit amplification of microbunching, which typically must be controlled through the otherwise unwanted addition of slice energy spread by use of a laser heater. Both emittance dilution and microbunching are mediated through coherent synchrotron radiation that occurs within a bunch compression chicane. In this paper we introduce a new option for bunch compressors, that of full arc compression, and compare it to the standard four-dipole chicane and to a recently proposed variant, the five-dipole CSR mitigating chicane. It is shown that the arc compressor and the five-dipole chicane are able to give greatly improved XFEL performance compared to the standard four-dipole chicane, both in soft and hard X-ray regimes. This is demonstrated in the context of two proposed XFELs, SXL at MAX-IV, Sweden, and UK-XFEL. It is further shown that the optimal choice of compression option depends on the particular FEL scheme. This means that a simultaneous multi-FEL facility, such as UK-XFEL, must implement both arc and five-dipole methods and must be able to select between them on a bunch-by-bunch basis scheme. This means that a simultaneous multi-FEL facility, such as UK-XFEL, must implement both arc and five-dipole methods and must be able to select between them on a bunch-by-bunch basis.

physics.acc-ph

Optics tuning simulations for FCC-ee using Python Accelerator Toolbox

The development of ultra-low emittance storage rings, such as the e+/e- Future Circular Collider (FCC-ee) with a circumference of about 90 km, aims to achieve unprecedented luminosity and beam size. One significant challenge is correcting the optics, which becomes increasingly difficult as we target lower emittances. In this paper, we investigate optics correction methods to address these challenges. We examined the impact of arc region magnet alignment errors in the baseline optics for the FCC-ee lattice at Z energy. To establish realistic alignment tolerances, we developed a sequence of correction steps using the Python Accelerator Toolbox (PyAT) to correct the lattice optics, achieve the nominal emittance, Dynamic Aperture (DA), and in the end, the design luminosity. The correction scheme has been recently optimized and better machine performance demonstrated. A comparison was conducted between two optics correction approaches: Linear Optics from Closed Orbits (LOCO) with phase advance + $η_x$ and coupling Resonance Driving Terms (RDTs) + $η_y$. The latter method demonstrated better performance in achieving the target emittance and enhancing the DA.

physics.acc-ph

Synthetic images aid the recognition of human-made art forgeries

Previous research has shown that Artificial Intelligence is capable of distinguishing between authentic paintings by a given artist and human-made forgeries with remarkable accuracy, provided sufficient training. However, with the limited amount of existing known forgeries, augmentation methods for forgery detection are highly desirable. In this work, we examine the potential of incorporating synthetic artworks into training datasets to enhance the performance of forgery detection. Our investigation focuses on paintings by Vincent van Gogh, for which we release the first dataset specialized for forgery detection. To reinforce our results, we conduct the same analyses on the artists Amedeo Modigliani and Raphael. We train a classifier to distinguish original artworks from forgeries. For this, we use human-made forgeries and imitations in the style of well-known artists and augment our training sets with images in a similar style generated by Stable Diffusion and StyleGAN. We find that the additional synthetic forgeries consistently improve the detection of human-made forgeries. In addition, we find that, in line with previous research, the inclusion of synthetic forgeries in the training also enables the detection of AI-generated forgeries, especially if created using a similar generator.

cs.CV