# Deep neural networks for classifying complex features in diffraction images

## TL;DR

This paper demonstrates that deep neural networks significantly improve the classification and feature recognition of complex diffraction images from nano-sized objects, streamlining data analysis in high-throughput diffraction experiments.

## Contribution

It presents a systematic setup, modifications, and benchmarking of deep neural networks for diffraction image classification, showing superior performance over previous methods.

## Key findings

- Deep neural networks outperform traditional algorithms in classifying diffraction patterns.
- The approach significantly reduces the effort in post-processing large diffraction datasets.
- Neural networks generalize well across different experimental conditions.

## Abstract

Intense short-wavelength pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nano-sized objects with a single x-ray laser shot. The enormous data sets with up to several million diffraction patterns represent a severe problem for data analysis, due to the high dimensionality of imaging data. Feature recognition and selection is a crucial step to reduce the dimensionality. Usually, custom-made algorithms are developed at a considerable effort to approximate the particular features connected to an individual specimen, but facing different experimental conditions, these approaches do not generalize well. On the other hand, deep neural networks are the principal instrument for today's revolution in automated image recognition, a development that has not been adapted to its full potential for data analysis in science. We recently published in Langbehn et al. (Phys. Rev. Lett. 121, 255301 (2018)) the first application of a deep neural network as a feature extractor for wide-angle diffraction images of helium nanodroplets. Here we present the setup, our modifications and the training process of the deep neural network for diffraction image classification and its systematic benchmarking. We find that deep neural networks significantly outperform previous attempts for sorting and classifying complex diffraction patterns and are a significant improvement for the much-needed assistance during post-processing of large amounts of experimental coherent diffraction imaging data.