Benchmarking deep learning for automated peak detection on GIWAXS data.

Area of Science:

• Materials Science and Condensed Matter Physics
• Crystallography and Scattering Techniques
• Data Science and Machine Learning Applications

Background:

• Rapid advancements in X-ray sources and detectors generate massive datasets, necessitating automated data processing.
• Real-time grazing-incidence wide-angle X-ray scattering (GIWAXS) experiments produce hundreds of thousands of images daily at synchrotron beamlines.
• Deep learning (DL) peak-detection shows promise but lacks rigorous benchmarking due to limited annotated datasets and standardized metrics.

Purpose of the Study:

• To establish a comprehensive framework for evaluating DL-based peak-detection techniques in GIWAXS.
• To address the need for annotated datasets, standardized metrics, and baseline models in GIWAXS data analysis.
• To benchmark DL solutions against classical algorithms using experimental data.

Main Methods:

• Development of a comprehensive framework including an annotated experimental GIWAXS dataset.
• Implementation of physics-informed metrics tailored for GIWAXS geometry.
• Optimization of a classical, non-DL peak-detection algorithm as a baseline.

Main Results:

• A recent DL solution, trained on simulated data, demonstrated superior performance compared to the optimized classical baseline.
• The developed framework facilitated a rigorous comparison of DL and classical methods.
• The study validated the effectiveness of DL for identifying diffraction peaks in complex GIWAXS data.

Conclusions:

• The proposed framework provides essential tools for reliable benchmarking of peak-detection algorithms in GIWAXS.
• Deep learning methods show significant potential for automating and enhancing the analysis of large-scale GIWAXS datasets.
• Further development of DL solutions can be guided by insights gained from this benchmarking study.