Machine learning in analytical spectroscopy for nuclear diagnostics [Invited]
Abstract
Analytical spectroscopy methods have shown many possible uses for nuclear material diagnostics and measurements in recent studies. In particular, the application potential for various atomic spectroscopy techniques is uniquely diverse and generates interest across a wide range of nuclear science areas. Over the last decade, techniques such as laser-induced breakdown spectroscopy, Raman spectroscopy, and x-ray fluorescence spectroscopy have yielded considerable improvements in the diagnostic analysis of nuclear materials, especially with machine learning implementations. These techniques have been applied for analytical solutions to problems concerning nuclear forensics, nuclear fuel manufacturing, nuclear fuel quality control, and general diagnostic analysis of nuclear materials. The data yielded from atomic spectroscopy methods provide innovative solutions to problems surrounding the characterization of nuclear materials, particularly for compounds with complex chemistry. Implementing these optical spectroscopy techniques can provide comprehensive new insights into the chemical analysis of nuclear materials. In particular, recent advances coupling machine learning methods to the processing of atomic emission spectra have yielded novel, robust solutions for nuclear material characterization. This review paper will provide a summation of several of these recent advances and will discuss key experimental studies that have advanced the use of analytical atomic spectroscopy techniques as active tools for nuclear diagnostic measurements.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 14, 2023
- Source ID
- 10.1364/ao.482533
Entities
People
- Anil K. Patnaik
- Ashwin P. Rao
- J D Auxier
- Michael B Shattan
- Phillip R. Jenkins
- Ryan E. Pinson
Organizations
- Air Force Institute of Technology
- Air Force Office of Scientific Research
- Air Force Research Laboratory
- Air Force Technical Applications Center
- Defense Threat Reduction Agency
- Los Alamos National Laboratory
- National Nuclear Security Administration