Publications

Abstract not provided.

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.

Two surface chemical explosive tests were observed for the Large Surface Explosion Coupling Experiment (LSECE) at the Nevada National Security Site in October 2020. The tests consisted of two one-ton explosions, one occurring before dawn and one occurring mid- afternoon. LSECE was performed in the same location as previous underground tests and aimed to explore the relationship between surface and underground explosions in support of global nonproliferation efforts. Several pieces of remote sensing equipment were deployed from a trailer 2.02 km from ground zero including high-speed cameras, radiometers and a spectrometer. The data collected from these tests will increase the knowledge of large surface chemical explosive signatures.