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DOE OSTI2024

Time series methods for the analysis of soundscapes and other cyclical ecological data

Biodiversity monitoring has entered an era of ‘big data’, exemplified by a near-continuous collection of sounds, images, chemical and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analysing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underlying, cyclic behaviour of the soundscape for each site, we show how one can identify circadian patterns in acoustic activity. Soundscapes in the dry season had a complex diel cycle, requiring multiple harmonics to represent daily variation, while in the wet season there was less variance attributable to the daily cyclic patterns. Our framework can be applied to most continuous, or near-continuous ecological data collected at a fine temporal resolution, allowing ecologists to explore patterns of temporal autocorrelation at multiple levels for biologically meaningful trends. Such methods will become indispensable as biological big data are used to understand the impact of anthropogenic pressures on biodiversity and to inform efforts to mitigate them.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

An accurate and efficient Ehrenfest dynamics approach for calculating linear and nonlinear electronic spectra

Linear and nonlinear electronic spectra provide an important tool to probe the absorption and transfer of electronic energy. Here, we introduce a pure state Ehrenfest approach to obtain accurate linear and nonlinear spectra that is applicable to systems with large numbers of excited states and complex chemical environments. We achieve this by representing the initial conditions as sums of pure states and unfolding multi-time correlation functions into the Schrödinger picture. By doing this, we show that one can obtain significant improvements in accuracy over the previously used projected Ehrenfest approach and that these benefits are particularly pronounced in cases where the initial condition is a coherence between excited states. While such initial conditions do not arise when calculating linear electronic spectra, they play a vital role in capturing multidimensional spectroscopies. We demonstrate the performance of our method by showing that it is able to quantitatively capture the exact linear, 2D electronic spectroscopy, and pump–probe spectra for a Frenkel exciton model in slow bath regimes and is even able to reproduce the main spectral features in fast bath regimes.

Chemistry↗

DOE OSTI2021

Searching for axionlike particles from core-collapse supernovae with Fermi LAT’s low-energy technique

Light axionlike particles (ALPs) are expected to be abundantly produced in core-collapse supernovae (CCSNe), resulting in a ~ 10 -second long burst of ALPs. These particles subsequently undergo conversion into gamma rays in external magnetic fields to produce a long gamma-ray burst (GRB) with a characteristic spectrum peaking in the 30–100-MeV energy range. At the same time, CCSNe are invoked as progenitors of ordinary long GRBs, rendering it relevant to conduct a comprehensive search for ALP spectral signatures using the observations of long GRBs with the Fermi Large Area Telescope (LAT). We perform a data-driven sensitivity analysis to determine CCSN distances for which a detection of an ALP signal is possible with the LAT’s low-energy technique which, in contrast to the standard LAT analysis, allows for a a larger effective area for energies down to 30 MeV. Assuming an ALP mass m a ≲ 10 - 10 eV and ALP-photon coupling g a γ = 5.3 × 10 - 12 GeV - 1 , values considered and deduced in ALP searches from SN1987A, we find that the distance limit ranges from ~ 0.5 to ~ 10 Mpc , depending on the sky location and the CCSN progenitor mass. Furthermore, we select a candidate sample of 24 GRBs and carry out a model comparison analysis in which we consider different GRB spectral models with and without an ALP signal component. We find that the inclusion of an ALP contribution does not result in any statistically significant improvement of the fits to the data. Finally, we discuss the statistical method used in our analysis and the underlying physical assumptions, the feasibility of setting upper limits on the ALP-photon coupling, and give an outlook on future telescopes in the context of ALP searches.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Rodeo Algorithm for Quantum Computing

We present a stochastic quantum computing algorithm that can prepare any eigenvector of a quantum Hamiltonian within a selected energy interval $\ [E-\epsilon, E+\epsilon]$. In order to reduce the spectral weight of all other eigenvectors by a suppression factor δ, the required computational effort scales as $\ O[|\log \delta|/(p \epsilon)]$, where p s the squared overlap of the initial state with the target eigenvector. The method, which we call the rodeo algorithm, uses auxiliary qubits to control the time evolution of the Hamiltonian minus some tunable parameter E . In this manner, we converge to the target eigenvector with exponential accuracy in the number of measurements. In addition to preparing eigenvectors, the method can also compute the full spectrum of the Hamiltonian. We illustrate the performance with several examples. For energy eigenvalue determination with error $\epsilon$, the computational scaling is $\ O[(\log \epsilon)^2/(p \epsilon)]$. For eigenstate preparation, the computational scaling is $\ O(\log \Delta/p)$, where $\Delta$ is the magnitude of the orthogonal component of the residual vector. The speed for eigenstate preparation is exponentially faster than that for phase estimation or adiabatic evolution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Filtered Backprojection in Compton Imaging using a Spherical Harmonic Wiener Filter with Pixelated CdZnTe

Filtered backprojection is an image reconstruction technique for Compton imaging that provides reasonably high resolution at much lower computational costs when compared to iterative methods. Here, this work applies a Wiener filter that has been derived for spherical harmonics on Compton imaging using the OrionUM pixelated CdZnTe imaging-spectrometer. To regularize the filter, an investigation is made into the power spectral density of the signal and noise to develop an appropriate spectral signal-to-noise ratio model for the restoration process. Experimental measurements were conducted with two 228Th sources placed 30° apart. The resulting filtered image of the two sources have an average full-width-at-half-maximum (FWHM) of 9.8° or 7.5° when using a mean squared error and structural similarity optimization approach respectively; an improvement from the 29.0° FWHM image when using simple backprojection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

HIGH-FIDELITY SIMULATION OF SOOT FORMATION AND THERMAL RADIATION IN A LABORATORY-SCALE RICH-QUENCH-LEAN BURNER

High-fidelity simulations of a swirl-stabilized turbulent spray flame in a laboratory-scale aero-combustor have been performed to evaluate the predictability of state-of-the-art models in capturing soot formation. The simulations employ a complex chemical mechanism developed for Jet-A with PAH chemistry, coupled with the Hybrid Method of Moments (HMOM) soot model, and a Lagrangian dilute spray model for the fuel injection. Two simulations are performed to compare the results when thermal radiation is neglected or included in the solution with a mean spectral model. Modeling closures for the soot differential diffusion effects in mixture fraction space, as well as turbulence-radiation interaction are also evaluated using the data generated by the simulations. Given the degree of complexity of the simulation, the results showed good agreement with experimental measurements of the spatial distribution of the soot volume fraction ensemble average. A closer agreement with the experiment is observed when thermal radiation is included in the solution. Thermal radiation is observed to reduce the flame temperature and increase the flame intermittency, denoted by the increase in the temperature standard deviation in mixture fraction space. The reduction in temperature also leads to a reduction in PAH production and soot volume fraction. Turbulence is observed to have different effects on radiative emission depending on the mixture fraction. Turbulent scalar fluctuations significantly enhance radiative emission in fuel lean mixtures and can also play a role for fuel rich conditions. The statistical description of the turbulence-radiation interaction, previously proposed in the literature, was observed to correctly reproduce the high-fidelity results. Model coefficients were provided for swirl-stabilized flames. The soot differential diffusion model, previously proposed in the literature, based on the residual between the exact term and its model approximation, was also evaluated. The residual correction term further improved the agreement with exact differential diffusion term evaluated with the high-fidelity simulation data in mixture fraction space. The results suggest that the effective turbulent Lewis number can be equal to unity in simulations of turbulent non-premixed recirculating flames.

Soriano, Bruno [Sandia National Laboratories (SNL)↗

DOE OSTI2024

ARM shortwave spectrometers to study the clear-cloud transition zone and mixing processes

The proposed research is a collaborative effort between NASA/Goddard Space Flight Center and the Hebrew University of Jerusalem. While the NASA team focused on analyzing ground-based hyper-spectral radiance observations to understand cloud edge properties and their connection to mixing processes, the Hebrew University team tackled the problem through cloud modeling activities. By approximating the shortwave spectra in the cloud-clear transition zone as a linear combination of purely clear and purely cloudy spectra we can characterize the variations of cloud optical thickness and cloud droplet effective radius in the transition zone. When applying this method to the measurements of a ground-based shortwave spectroradiometer at the ARM’s SGP site, representing continental conditions, and MAGIC field campaign between Log Angeles, California and Honolulu, Hawaii, representing maritime scenarios, we found that cloud optical depth consistently decreases in both cases, but droplet size decreases much more substantially for the continental regime, suggesting different mixing processes for the continental and maritime conditions. The investigation and measurements of radiation clouds were coupled with a unique cloud modeling. A novel spectral bin microphysics was developed and implemented to the System of Atmospheric Modeling (SAM). In order to resolve cloud transition zones with high spatial gradients of microphysical variables a unique high resolution (10 m) was used in simulations. The model calculates droplet size distributions in each grid point. The model output was transferred to the NASA/GSFC team for utilization in radiative calculations and testing of both radiative algorithm and model representation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Improved galactic foreground removal for B-mode detection with clustering methods

ABSTRACT Characterizing the sub-mm Galactic emission has become increasingly critical especially in identifying and removing its polarized contribution from the one emitted by the cosmic microwave background (CMB). In this work, we present a parametric foreground removal performed on to sub-patches identified in the celestial sphere by means of spectral clustering. Our approach takes into account efficiently both the geometrical affinity and the similarity induced by the measurements and the accompanying errors. The optimal partition is then used to parametrically separate the Galactic emission encoding thermal dust and synchrotron from the CMB one applied on two nominal observations of forthcoming experiments from the ground and from the space. Moreover, the clustering is performed on tracers that are different from the data used for component separation, e.g. the spectral index maps of dust and synchrotron. Performing the parametric fit singularly on each of the clustering derived regions results in an overall improvement: both controlling the bias and the uncertainties in the CMB B-mode recovered maps. We finally apply this technique using the map of the number of clouds along the line of sight, $\mathcal {N}_c$, as estimated from H i emission data and perform parametric fitting on to patches derived by clustering on this map. We show that adopting the $\mathcal {N}_c$ map as a tracer for the patches related to the thermal dust emission, results in reducing the B-mode residuals post-component separation. The code is made publicly available https://github.com/giuspugl/fgcluster.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Floquet dynamics of disordered bands with isolated critical energies

We investigate localization properties of driven models which exhibit a subextensive number of extended states in the static setting. We consider instances where the extended modes are or are not protected by topological considerations. To this end, we contrast the strongly driven, disordered, lowest Landau level, which we refer to as the random Landau model (RLM), with the random dimer model (RDM); the RDM also has a subextensive set of delocalized modes in the middle of the spectrum whose origin is not topological. We map the driven models on to a higher-dimensional effective model and numerically compute the localization length as a function of disorder strength, drive amplitude, and frequency using the recursive Green's function method. Our numerical results indicate that, in the presence of a strong drive (low frequency and/or large drive amplitude), the topologically protected RLM continues to exhibit a spectrum with both localized and delocalized (or critical) modes, but the spectral range of delocalized modes is enhanced by the driving. This occurs due to an admixture of the localized modes with extended modes arising due to the topologically protected critical energy in the middle of the spectrum. On the other hand, in the RDM, a weak drive immediately localizes the entire spectrum. This occurs in contrast to the naive expectation from perturbation theory that mixing between localized and delocalized modes generically enhances the delocalization of all modes. Our work highlights the importance of the origin of the delocalized modes in the localization properties of the corresponding Floquet model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Demystifying the Diffuse Vibrational Spectrum of Aqueous Protons Through Cold Cluster Spectroscopy

The ease with which the pH is routinely determined for aqueous solutions masks the fact that the cationic product of Arrhenius acid dissolution, the hydrated proton, or H + (aq), is a remarkably complex species. Here, we review how results obtained over the past 30 years in the study of H + ·(H 2 O) n cluster ions isolated in the gas phase shed light on the chemical nature of H + (aq). This effort has also revealed molecular-level aspects of the Grotthuss relay mechanism for positive-charge translocation in water. Recently developed methods involving cryogenic cooling in radiofrequency ion traps and the application of two-color, infrared–infrared (IR–IR) double-resonance spectroscopy have established a clear picture of how local hydrogen-bond topology drives the diverse spectral signatures of the excess proton. This information now enables a new generation of cluster studies designed to unravel the microscopic mechanics underlying the ultrafast relaxation dynamics displayed by H + (aq).

Chemistry↗

DOE OSTI2024

Image Deconvolution and Point-spread Function Reconstruction with STARRED: A Wavelet-based Two-channel Method Optimized for Light-curve Extraction

We present starred, a point-spread function (PSF) reconstruction, two-channel deconvolution, and light-curve extraction method designed for high-precision photometric measurements in imaging time series. An improved resolution of the data is targeted rather than an infinite one, thereby minimizing deconvolution artifacts. In addition, starred performs a joint deconvolution of all available data, accounting for epoch-to-epoch variations of the PSF and decomposing the resulting deconvolved image into a point source and an extended source channel. The output is a high-signal-to-noise-ratio, high-resolution frame combining all data and the photometry of all point sources in the field of view as a function of time. Of note, starred also provides exquisite PSF models for each data frame. We showcase three applications of starred in the context of the imminent LSST survey and of JWST imaging: (i) the extraction of supernovae light curves and the scene representation of their host galaxy; (ii) the extraction of lensed quasar light curves for time-delay cosmography; and (iii) the measurement of the spectral energy distribution of globular clusters in the "Sparkler," a galaxy at redshift z = 1.378 strongly lensed by the galaxy cluster SMACS J0723.3-7327. starred is implemented in jax, leveraging automatic differentiation and graphics processing unit acceleration. This enables the rapid processing of large time-domain data sets, positioning the method as a powerful tool for extracting light curves from the multitude of lensed or unlensed variable and transient objects in the Rubin-LSST data, even when blended with intervening objects.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Expansion dynamics and chemistry evolution in ultrafast laser filament produced plasmas

Laser ablation in conjunction with optical emission spectroscopy is a potential non-contact, standoff detection method for all elements in the periodic table and certain isotopes such as radionuclides. Currently, significant development efforts are on-going to use ultrafast laser filaments for remote sensing. The application of filaments is of particular interest in extending the range of stand-off capability associated with elemental and isotopic detection via laser-induced breakdown spectroscopy. In this study, we characterize the expansion dynamics and chemical evolution of filament-produced uranium (U) plasmas. Laser filaments are generated in the laboratory by loosely focusing 35 femtosecond (fs), 6 milli Joule (mJ) pulses in air. Two-dimensional spectrally-integrated and time-resolved imaging was performed to study hydrodynamics and evolution of U atomic and UO molecular emission in filament-produced U plasmas. Our results highlight that filament ablation of U plasmas gives a cylindrical plasma plume morphology with an appearance of plume splitting into slow and fast moving components at later times of its evolution. Emission from the slow-moving region shows no distinct spectral features (i.e. broadband-like) and is contributed in part by nanoparticles generated during ultrafast laser ablation. Additionally, we find U atoms and U oxide molecules (i.e. UO, UxOy) co-exist in the filament produced plasma, which can be attributed to the generation of low-temperature plasma conditions during filament ablation

(140.3440) Laser-induced breakdown, (280.5395) Pla↗

DOE OSTI2022

Real-space density kernel method for Kohn–Sham density functional theory calculations at high temperature

Kohn–Sham density functional theory calculations using conventional diagonalization based methods become increasingly expensive as temperature increases due to the need to compute increasing numbers of partially occupied states. In this work, we present a density matrix based method for Kohn–Sham calculations at high temperatures that eliminates the need for diagonalization entirely, thus reducing the cost of such calculations significantly. Specifically, we develop real-space expressions for the electron density, electronic free energy, Hellmann–Feynman forces, and Hellmann–Feynman stress tensor in terms of an orthonormal auxiliary orbital basis and its density kernel transform, the density kernel being the matrix representation of the density operator in the auxiliary basis. Using Chebyshev filtering to generate the auxiliary basis, we next develop an approach akin to Clenshaw–Curtis spectral quadrature to calculate the individual columns of the density kernel based on the Fermi operator expansion in Chebyshev polynomials and employ a similar approach to evaluate band structure and entropic energy components. We implement the proposed formulation in the SPARC electronic structure code, using which we show systematic convergence of the aforementioned quantities to exact diagonalization results, and obtain significant speedups relative to conventional diagonalization based methods. Finally, we employ the new method to compute the self-diffusion coefficient and viscosity of aluminum at 116 045 K from Kohn–Sham quantum molecular dynamics, where we find agreement with previous more approximate orbital-free density functional methods.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Degradation in Photovoltaic Encapsulant Transmittance: Results of the Second PVQAT TG5 Artificial Weathering Study

The optical degradation of encapsulants from ultraviolet (UV) radiation has historically resulted in a significant loss in performance throughout the life of a photovoltaic (PV) module. International Electrotechnical Commission (IEC) test methods have recently been developed to screen for PV encapsulants prone to loss in optical performance. The present study was performed to benchmark polymeric packaging materials relative to IEC 62788-1-4 (covering the measurement of optical transmittance) and IEC 62788-1-7 (on the durability of transmittance), provide feedback toward improvement of the methods, and develop insight regarding optical degradation. Contemporary materials were examined, including poly(ethylene-co-vinyl acetate) (EVA), thermoplastic polyolefin (TPO), polyolefin elastomer (POE), and polyvinyl butyral (PVB) encapsulants; a poly(ethene-co-tetrafluoroethene)/poly(ethylene terephthalate) (ETFE/PET) transparent backsheet; and a polystyrene (PS) working reference material. The use of silica-, specialty-, and rolled-glass was also compared in laminated coupons. Specimen size was separately examined from 2.5 to 12.5 cm. Weathering was performed with a xenon source, using IEC TS 62788-7-2 methods A2, A3, A4, and A5 (chamber temperature of 55 degrees C, 65 degrees C, 75 degrees C, or 85 degrees C), respectively. Characterizations were made using a UV-visible-near-infrared (UV-VIS-NIR) spectrophotometer (transmittance and reflectance, with and without an integrating sphere), a UV-VIS fluorescence spectrophotometer, a camera, and an optical microscope. Performance was analyzed, including solar weighted transmittance, yellowness index, UV cut-off wavelength, and haze (scattering). Separate Arrhenius analyses were performed to assess retention of transmittance and changes in yellowness index. The activation energy for both characteristics was found to range from 15-80 kJ mol-1, with an average of 48 kJ mol-1, similar to the average of 45 kJ mol-1 identified in the previous international PV Quality Assurance Task Force (PVQAT) Task Group 5 (TG5) study of more traditional encapsulants. The separate degradation modes of discoloration and scattering were distinguished in the encapsulants using a comprehensive spectral characterization. Based on these results, the IEC 62788-1-7 pass/fail criteria of 5% change in transmittance was confirmed to identify a known bad encapsulant.

durability↗

DOE OSTI2020

Design, characterization and indoor validation of the optical soiling detector “DUSST”

Nowadays, photovoltaic (PV) technology has reached a high level of maturity in terms of module efficiency and cost competitiveness in comparison with other energy technologies. As PV has achieved high levels of deployment, the development of devices that can help to reduce PV operation and maintenance costs has become a priority. Soiling can be cause of significant losses in certain PV plants and its detection has become essential to ensure a correct mitigation. For this reason, accurate and low-cost monitoring devices are needed. While soiling stations have been traditionally employed to measure the impact of soiling, their high cost and maintenance have led to the development of innovative low-cost optical sensors, such as the device presented in this work and named “DUSST” (Detector Unit for Soiling Spectral Transmittance). The thermal characterization of DUSST’s components and the methodology used to predict soiling transmittance losses are presented in this study. The results demonstrate that the losses can be predicted with an error lower than 1.4%. The method has been verified with an experimental campaign with naturally soiled coupons exposed outdoors in Jaén, Spain.

14 SOLAR ENERGY↗

DOE OSTI2026

Comparability of Liquid Chromatography Tandem Mass Spectrometry Analysis of Dissolved Organic Matter across Laboratories

Non-targeted liquid chromatography tandem highresolution mass spectrometry (LC−MS/MS) is increasingly applied for the structure-resolved chemical analysis of dissolved organic matter (DOM). With new developments in MS instrumentation and analysis software, the approach has gained substantial momentum over the past decade. However, achieving high-quality analytical data that is reproducible and comparable across laboratories can be a bottleneck in non-targeted metabolomics and organic matter chemical analysis, especially for data reuse in repository-scale analyses. Understanding the capabilities as well as challenges of comparing LC−MS/MS data from different laboratories is necessary for inferring global trends from public data sets. To illuminate instrumentation factors that drive differences and variability, we used a standardized data analysis pipeline, including classical (CMN) and featurebased molecular networking (FBMN), to analyze data from a ring trial by 24 laboratories on identical sample sets of algal and DOM extracts that were mixed in predefined concentrations and spiked with standards. Our results showed that data sets from similar mass spectrometer types with unified instrument parameters were qualitatively comparable, resolving the same general trends and shared mass spectral features. Interlaboratory comparability was best for high-intensity features, while low-intensity features showed greater detection variability. Our analysis also highlights challenges when comparing data from instruments with different acquisition rates or operating with less standardized methods. Lastly, we provide recommendations for data integration, public data sharing, standardization, and best practices for standardized LC−MS/MS data acquisition, which will be critical for long-term time series and intercomparability of DOM chemical analyses.

DOM↗

DOE OSTI2026

Transient Pulse-Response Time-of-Flight Mass Spectrometry for Complex, Deactivating Heterogeneous Catalytic Systems: Application to Ethane Dehydroaromatization

The study of complex, multistep bond-forming and -breaking reactions in heterogeneous catalytic systems often encounters challenges associated with the involvement of large numbers of intermediates among branching pathways. Kinetic information obtained from traditional steady-state measurements can be complemented with that from time-resolved methods to uncover details of the underlying chemistry. Herein, we describe an approach for tracking the complete time-resolved chemical composition (ca. 4–200 u) of a reactor effluent in response to a reactant pulse. We use a six-port rotary valve with a metered sampling loop to pulse reactants at ambient pressure into a flow reactor packed with a catalyst bed within the isothermal region of a heated furnace. The temporal evolution of effluent species is tracked using time-resolved molecular-beam time-of-flight mass spectrometry. We highlight the possibilities that this method has to offer by studying the complex bifunctional mechanism of ethane dehydroaromatization over an HZSM-5-supported platinum catalyst. We demonstrate that energy-tunable ionization sources, which facilitate isomer resolution, enable the measurement of the full mass spectral time-dependent system response. This includes the evolution of major products and mechanistically relevant reactive intermediates such as 1,3-butadiene and cyclopentadiene; these species have not previously been observed from this reaction. In additional studies, we also assess the role of platinum in the catalyst by examining temporal responses to ethane and ethylene feeds. Results show that two temporally distinct formation pathways exist for methane and benzene, and that their importance depends on both catalyst composition and reactant identity. Additionally, characteristics of catalyst deactivation are uniquely observable in the time-resolved mass spectral response, including the selective deactivation of a benzene formation pathway. The combination of time-of-flight mass spectrometry with tunable ionization enables the simultaneous observation of all effluents in a complex mixture of intermediates with isomer/isobar differentiation capabilities that can be applied to any complex reaction system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

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↗