Unsupervised real-world knowledge extraction via disentangled variational autoencoders for photon diagnostics.

Area of Science:

• Physics
• Data Science
• Spectroscopy

Background:

• Free electron lasers (FELs) generate intense X-ray pulses for scientific research.
• Accurate online monitoring of FEL spectra is crucial for experimental success.
• Processing low signal-to-noise ratio (SNR) data presents significant analytical challenges.

Purpose of the Study:

• To apply unsupervised neural networks for processing electron time-of-flight data from FEL diagnostics.
• To develop a method for extracting interpretable information from low-SNR FEL spectra.
• To enhance the quality and interpretability of diagnostic analysis at the free electron laser FLASH.

Main Methods:

• Utilized disentangled variational autoencoders (VAEs) for unsupervised learning.
• Applied the method to real-world electron time-of-flight data from the FLASH facility.
• Focused on online wavelength monitoring and spectral analysis.

Main Results:

• The VAE successfully identified representations of single-shot FEL spectra with low SNR.
• The network revealed human-interpretable information on photon energy, intensity, and detector-specific features.
• The method demonstrated effective data cleaning, including denoising and artifact removal, enhancing the identification of low-intensity signatures.

Conclusions:

• Unsupervised neural networks, specifically disentangled VAEs, offer a powerful tool for analyzing complex spectroscopic data.
• This approach significantly improves diagnostic analysis quality and data interpretability at FEL facilities.
• The method holds potential for broader applications in analyzing similar spectroscopy datasets.