EDS Data Analytics
The Data Processing infrastructure provides comprehensive tools and frameworks for scientists to efficiently analyze, process, and extract meaningful insights from experimental data collected at LCLS facilities.
Built around C++ and Python-based analysis frameworks, specialized data formats, and purpose-built feedback tools, this infrastructure supports the entire data analysis lifecycle - from initial data exploration and calibration to advanced scientific analysis and visualization of complex experimental outcomes.
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Our Data Processing systems transforms raw detector outputs into publication-ready scientific results.
psana1/psana2
Python-based analysis framework for LCLS data.
Learn more about psana1
Learn more about psana2
xtc1/xtc2
Data format specifications for LCLS experimental data.
Learn more about xtc format
See the GitHub Repo
Tools to facilitate production of calibrated, aligned LCLS HDF5 files that are an input to user analysis pipelines.
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MLCV for Data Processing
Machine Learning approaches to enhance data processing:
- Neural networks for data classification
- ML-based noise reduction and filtering
- Automated feature extraction
