RESUMEN
The removal or cancellation of noise has wide-spread applications in imaging and acoustics. In applications in everyday life, such as image restoration, denoising may even include generative aspects, which are unfaithful to the ground truth. For scientific use, however, denoising must reproduce the ground truth accurately. Denoising scientific data is further challenged by unknown noise profiles. In fact, such data will often include noise from multiple distinct sources, which substantially reduces the applicability of simulation-based approaches. Here we show how scientific data can be denoised by using a deep convolutional neural network such that weak signals appear with quantitative accuracy. In particular, we study X-ray diffraction and resonant X-ray scattering data recorded on crystalline materials. We demonstrate that weak signals stemming from charge ordering, insignificant in the noisy data, become visible and accurate in the denoised data. This success is enabled by supervised training of a deep neural network with pairs of measured low- and high-noise data. We additionally show that using artificial noise does not yield such quantitatively accurate results. Our approach thus illustrates a practical strategy for noise filtering that can be applied to challenging acquisition problems.
RESUMEN
An on-board sample cleaver has been developed in order to cleave small and hard-to-cleave samples. To acquire good cleaves from rigid samples the alignment of the cleaving blade with respect to the internal crystallographic planes is crucial. To have the opportunity to mount the sample and align it to the blade ex situ has many advantages. The design presented has allowed us to cleave very tiny and rigid samples, e.g., the high-temperature superconductor La((2-x))Sr(x)CuO(4). Further, in this design the sample and the cleaver will have the same temperature, allowing us to cleave and keep the sample at low temperature. This is a big advantage over prior cleaver systems. As a result, better surfaces and alignments can be realized, which considerably simplifies and improves the experiments.
