Spectral dynamics and gas-phase oxidation of laser-produced plutonium plasmas
Pu gas-phase oxidation and Pu oxide bands identified with Pu I spectral modeling and time-resolved excitation temperature of Pu plasma.
Harilal, S. S.↗
Engineering topics
Murzyn, Christopher M.
Publications and source records attributed to Murzyn, Christopher M..
Machine Learning Predictions of Transition Probabilities in Atomic Spectra
Forward modeling of optical spectra with absolute radiometric intensities requires knowledge of the individual transition probabilities for every transition in the spectrum. In many cases, these transition probabilities, or Einstein A-coefficients, quickly become practically impossible to obtain through either theoretical or experimental methods. Complicated electronic orbitals with higher order effects will reduce the accuracy of theoretical models. Experimental measurements can be prohibitively expensive and are rarely comprehensive due to physical constraints and sheer volume of required measurements. Due to these limitations, spectral predictions for many element transitions are not attainable. In this work, we investigate the efficacy of using machine learning models, specifically fully connected neural networks (FCNN), to predict Einstein A-coefficients using data from the NIST Atomic Spectra Database. For simple elements where closed form quantum calculations are possible, the data-driven modeling workflow performs well but can still have lower precision than theoretical calculations. For more complicated nuclei, deep learning emerged more comparable to theoretical predictions, such as Hartree–Fock. Unlike experiment or theory, the deep learning approach scales favorably with the number of transitions in a spectrum, especially if the transition probabilities are distributed across a wide range of values. It is also capable of being trained on both theoretical and experimental values simultaneously. In addition, the model performance improves when training on multiple elements prior to testing. The scalability of the machine learning approach makes it a potentially promising technique for estimating transition probabilities in previously inaccessible regions of the spectral and thermal domains on a significantly reduced timeline.
74 ATOMIC AND MOLECULAR PHYSICS↗
Hyperfine structures and isotopic shifts of uranium transitions using tunable laser spectroscopy of laser ablation plumes
We report isotopic shifts and hyperfine structures of selected U transitions employing tunable spectroscopy viz: laser-induced fluorescence and laser absorption spectroscopy of laser ablation plumes. The laser-produced plasmas were produced during ns laser ablation on a natural U metal which contains 0.7% 235U. Our results show that isotopic shifts between 238U and 235U are entangled with hyperfine structures of 235U. The isotopic ratio obtained using laser-induced fluorescence is affected by optical saturation effect. Time-resolved laser absorption spectroscopy is carried out for evaluating the optical absorption and estimating the hyperfine constants.
(020.3260) Isotope shifts↗