# Deep Learning Accelerated Light Source Experiments

arXiv: https://arxiv.org/abs/1910.04081 · HTML: https://mlsanity.com/paper/1910.04081/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 6 of 6)

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- [fairDMS: Rapid Model Training by Data and Model Reuse](https://mlsanity.com/paper/2204.09805/) (2022)
- [Masked Sinogram Model with Transformer for ill-Posed Computed Tomography Reconstruction: a Preliminary Study](https://mlsanity.com/paper/2209.01356/) (2022)
- [OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science](https://mlsanity.com/paper/2302.06751/) (2023)
- [AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources](https://mlsanity.com/paper/2304.04297/) (2023)

## Abstract (arXiv)

Experimental protocols at synchrotron light sources typically process and validate data only after an experiment has completed, which can lead to undetected errors and cannot enable online steering. Real-time data analysis can enable both detection of, and recovery from, errors, and optimization of data acquisition. However, modern scientific instruments, such as detectors at synchrotron light sources, can generate data at GBs/sec rates. Data processing methods such as the widely used computational tomography usually require considerable computational resources, and yield poor quality reconstructions in the early stages of data acquisition when available views are sparse. We describe here how a deep convolutional neural network can be integrated into the real-time streaming tomography pipeline to enable better-quality images in the early stages of data acquisition. Compared with conventional streaming tomography processing, our method can significantly improve tomography image quality, deliver comparable images using only 32% of the data needed for conventional streaming processing, and save 68% experiment time for data acquisition.