Machine learning in analytical spectroscopy for nuclear diagnostics [Invited]

Area of Science:

• Nuclear Science and Engineering
• Analytical Chemistry
• Materials Science

Background:

• Analytical spectroscopy methods are increasingly vital for nuclear material diagnostics.
• Atomic spectroscopy techniques show diverse applications across nuclear science.
• Recent advancements focus on improving diagnostic analysis of nuclear materials.

Purpose of the Study:

• To review recent advances in analytical atomic spectroscopy for nuclear material characterization.
• To discuss experimental studies highlighting the utility of these techniques.
• To explore the integration of machine learning with spectroscopy for nuclear applications.

Main Methods:

• Laser-induced breakdown spectroscopy (LIBS)
• Raman spectroscopy
• X-ray fluorescence (XRF) spectroscopy
• Machine learning algorithms for spectral data processing

Main Results:

• Significant improvements in diagnostic analysis of nuclear materials.
• Development of analytical solutions for nuclear forensics, fuel manufacturing, and quality control.
• Novel and robust characterization of nuclear materials, including complex compounds.
• Enhanced insights into the chemical analysis of nuclear materials through optical spectroscopy.

Conclusions:

• Analytical atomic spectroscopy, particularly when enhanced by machine learning, offers powerful tools for nuclear material diagnostics.
• These techniques provide innovative solutions for characterizing nuclear materials with complex chemistry.
• Continued research and implementation of these methods are crucial for advancing nuclear science and security.