# Ultrafast processing of pixel detector data with machine learning frameworks

## TL;DR

This paper explores the use of machine learning frameworks for ultrafast pixel detector data processing at FEL facilities, redesigning classical algorithms in TensorFlow to achieve significant speedups and cost reductions.

## Contribution

The authors developed TensorFlow implementations of classical algorithms, enabling faster, hardware-agnostic processing of pixel detector data, and provided insights for future deep learning architectures.

## Key findings

- Achieved 1-2 orders of magnitude faster processing on consumer GPUs.
- Reduced projected online analysis costs by 3 orders of magnitude.
- Classical algorithms outperformed deep learning models in noise reduction while preserving photon data.

## Abstract

Modern photon science performed at high repetition rate free-electron laser (FEL) facilities and beyond relies on 2D pixel detectors operating at increasing frequencies (towards 100 kHz at LCLS-II) and producing rapidly increasing amounts of data (towards TB/s). This data must be rapidly stored for offline analysis and summarized in real time. While at LCLS all raw data has been stored, at LCLS-II this would lead to a prohibitive cost; instead, enabling real time processing of pixel detector raw data allows reducing the size and cost of online processing, offline processing and storage by orders of magnitude while preserving full photon information, by taking advantage of the compressibility of sparse data typical for LCLS-II applications. We investigated if recent developments in machine learning are useful in data processing for high speed pixel detectors and found that typical deep learning models and autoencoder architectures failed to yield useful noise reduction while preserving full photon information, presumably because of the very different statistics and feature sets between computer vision and radiation imaging. However, we redesigned in Tensorflow mathematically equivalent versions of the state-of-the-art, "classical" algorithms used at LCLS. The novel Tensorflow models resulted in elegant, compact and hardware agnostic code, gaining 1 to 2 orders of magnitude faster processing on an inexpensive consumer GPU, reducing by 3 orders of magnitude the projected cost of online analysis at LCLS-II. Computer vision a decade ago was dominated by hand-crafted filters; their structure inspired the deep learning revolution resulting in modern deep convolutional networks; similarly, our novel Tensorflow filters provide inspiration for designing future deep learning architectures for ultrafast and efficient processing and classification of pixel detector images at FEL facilities.