The computation of partition functions and thermochemistry data for atomic, ionic, diatomic, and polyatomic species
Computation of specific free energy and enthalpy for atomic, diatomic, and polyatomic species from partition functions
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Computation of specific free energy and enthalpy for atomic, diatomic, and polyatomic species from partition functions
In this work, we have used an electron beam ion trap to measure electron-density-diagnostic line-intensity ratios for extreme ultraviolet lines from Fe xii, xiii, and xiv at wavelengths of ≈185–205 and 255–276 Å. These ratios can be used as density diagnostics for astrophysical spectra and are especially relevant to solar physics. We found that density diagnostics using the Fe xiii 196.53/202.04 and the Fe xiv 264.79/274.21 and 270.52A/274.21 line ratios are reliable using the atomic data calculated with the Flexible Atomic Code (FAC). On the other hand, we found a large discrepancy between the FAC theory and experiment for the commonly used Fe xii (186.85 + 186.88)/195.12 line ratio. These FAC theory calculations give results similar to the data tabulated in CHIANTI, which are commonly used to analyze solar observations. Our results suggest that the discrepancies seen between solar coronal density measurements using the Fe xii (186.85 + 186.88)/195.12 and Fe xiii 196.54/202.04 line ratios are likely due to issues with the atomic calculations for Fe xii.
Atom Probe Tomography (APT) is a powerful technique for visualizing the atomic-scale distribution of solutes in materials, but quantitative cluster analysis of APT datasets remains a challenge due to the need for subjective parameter selection in clustering algorithms. While distance-based and density-based methods such as HDBSCAN are widely used, their performance is highly sensitive to user-defined parameters, which undermines reproducibility and accuracy. This study proposes an image-based, deep learning-aided workflow for automating parameter selection and cluster detection in APT data analysis. By projecting 3D APT point clouds onto 2D planes, we leverage pretrained convolutional neural networks (ConvNeXt-Tiny and ResNet-50) through transfer learning to predict the number of clusters present in synthetic datasets. The output is used to guide K-means clustering and estimate HDBSCAN parameters, specifically minimum cluster size and minimum sample points. This approach reduces reliance on manual parameter tuning, improving consistency and scalability. The methodology demonstrates the feasibility of using image-based deep learning for interpreting complex spatial patterns in APT data, enabling faster and more objective analysis. The complete workflow and code are made publicly available to support reproducibility and future research.
Atom probe tomography (APT) has enabled the direct visualization of solute clusters, providing valuable insights into material structures. This clustering is crucial for understanding the nanoscale composition and behavior of materials, which can significantly influence their mechanical and physical properties. However, the widely used clustering methods in the APT community face challenges such as subjective parametric selection and limited applicability, particularly in dealing with overlapping clusters, nested clusters, and artifacts across different scales, such as precipitates and dislocations. To address these challenges, we present a framework based on density-based cluster analysis that aims to be less dependent on user input, reproducible, and robust.
The present volume on atomic and molecular spectroscopic data for space astrophysics discusses scientific problems and laboratory data needs associated with the Hubble Space Telescope, atomic data needed for far ultraviolet astronomy with HUT and FUSE and for analysis of EUV and X-ray spectra, and data for observations of interstellar medium with the Hubble Space Telescope. Attention is also given to atomic and molecular data for analysis of IR spectra from ISO and SIRTF, atomic data from the opacity project, sources of atomic spectroscopic data for astrophysics, and summary of current molecular data bases.
In this study, a refined approach for multicomponent fitting of active galactic nuclei (AGNs) spectra is presented utilizing the newly developed Python code Fully Automated pythoN Tool for AGN Spectra analYsis (fantasy). AGN spectra are modeled by simultaneously considering the underlying broken power-law continuum, predefined emission line lists, and an Fe II model, which is here extended to cover the wavelength range 3700–11000 Å. The Fe II model, founded solely on atomic data, effectively describes the extensive emission of the complex iron ion in the vicinity of the Hγ and Hβ lines, as well as near the Hα line, which was previously rarely studied. The proposed spectral fitting approach is tested on a sample of high-quality AGN spectra from the Sloan Digital Sky Survey Data Release 17. The results indicate that when Fe II emission is present near Hβ, it is also detected redward from Hα, potentially contaminating the broad Hα line wings and thus affecting the measurements of its flux and width. The production of Fe II emission is found to be strongly correlated with Eddington luminosity and appears to be controlled by a similar mechanism as the hydrogen Balmer lines. The study highlights the benefits of fitting type 1 AGN spectra with the fantasy code, pointing that it may be used as a robust tool for analyzing a large number of AGN spectra in the coming spectral surveys.
The interaction of a pyridine molecule with H and Cl atoms on a Si(111) surface is studied using a cluster model in conjunction with the B3LYP/6-3 1G level of theory. Regardless of the type of the neighboring atoms, the interaction of the pyridine with a hydrogen atom is very different from its interaction a chlorine atom. This system is discussed in terms of our atomic data storage proposal.
CHIANTI contains a large quantity of atomic data for the analysis of astrophysical spectra. Programs are available in IDL and Python to perform calculation of the expected emergent spectrum from these sources. The database includes atomic energy levels, wavelengths, radiative transition probabilities, rate coefficients for collisional excitation, ionization, and recombination, as well as data to calculate free–free, free–bound, and two-photon continuum emission. In Version 9, we improve the modeling of the satellite lines at X-ray wavelengths by explicitly including autoionization and dielectronic recombination processes in the calculation of level populations for select members of the lithium isoelectronic sequence and Fe XVIII–XXIII. In addition, existing data sets are updated, new ions are added, and new total recombination rates for several Fe ions are included. All data and IDL programs are freely available at http://www.chiantidatabase.org or through SolarSoft, and the Python code ChiantiPy is also freely available at https://github.com/chianti-atomic/ChiantiPy.
Atomic data and plasma models play a crucial role in the diagnosis and interpretation of astrophysical spectra, thus influencing our understanding of the Universe. In this investigation we present a systematic comparison of the leading photoionization codes to determine how much their intrinsic differences impact X-ray spectroscopic studies of hot plasmas in photoionization equilibrium. We carry out our computations using the Cloudy, SPEX, and XSTAR photoionization codes, and compare their derived thermal and ionization states for various ionizing spectral energy distributions. We examine the resulting absorption-line spectra from these codes for the case of ionized outflows in active galactic nuclei. By comparing the ionic abundances as a function of ionization parameter, we find that on average there is about 30 deviation between the codes in where ionic abundances peak. For H-like to B-like sequence ions alone, this deviation in is smaller at about 10 on average. The comparison of the absorption-line spectra in the X-ray band shows that there is on average about 30 deviation between the codes in the optical depth of the lines produced at log 1 to 2, reducing to about 20 deviation at log 3. We also simulate spectra of the ionized outflows with the current and upcoming high-resolution X-ray spectrometers, on board XMM-Newton, Chandra, Hitomi, and Athena. From these simulations we obtain the deviation on the best-fit model parameters, arising from the use of different photoionization codes, which is about 10 to40. We compare the modeling uncertainties with the observational uncertainties from the simulations. The results highlight the importance of continuous development and enhancement of photoionization codes for the upcoming era of X-ray astronomy with Athena.
We report measurements of electron-impact excitation cross sections for the strong K-shell n = 2 -> 1 transitions in S XV, using the LLNL EBIT-I electron beam ion trap, two crystal spectrometers, and the EBIT Calorimeter Spectrometer. The cross sections are determined by direct normalization to the well-known cross sections of radiative electron capture, measured simultaneously. Using contemporaneous polarization measurements with the two crystal spectrometers, whose dispersion planes are oriented parallel and perpendicular to the electron beam direction, the polarization of the direct excitation line emission is determined, and in turn the isotropic total cross sections are extracted. We further experimentally investigate various line-formation mechanisms, finding that radiative cascades and collisional inner-shell ionization dominate the degree of linear polarization and total line emission cross sections of the forbidden line, z.
While application of clustering algorithms to atom probe tomography data have enabled quantification of solute clusters in terms of number density, size, and subcomposition there exist other properties (e.g., volume, surface area, and composition) that are better determined by defining an interface between the cluster and the surrounding matrix. The limitation in composition results from an ion selection step where the expected matrix ion types are omitted from the cluster search algorithm to enhance the contrast between the matrix and cluster and to reduce the complexity of the search. Previously, composition determination within solute clusters has utilized a secondary envelopment and erosion step on top of conventional methods such as maximum separation. In this work, we present a novel stochastic method that combines the particle identification fidelity of a conventional clustering algorithm with the analytical flexibility of mesh-based approaches through the generation of alpha shapes for each identified cluster. Furthermore, the corresponding mesh accounts for concave components of the clusters and determines the volume and surface area of the clusters; additionally, the mesh boundary is utilized to update the total composition according to the internal ions.
The research activity comprised the measurement of basic atomic processes and parameters which relate directly to the interpretation of solar ultraviolet observations and to the development of comprehensive models of the component structures of the solar atmosphere. The research was specifically directed towards providing the relevant atomic data needed to perform and to improve solar diagnostic techniques which probe active and quiet portions of the solar chromosphere, the transition zone, the inner corona, and the solar wind acceleration regions of the extended corona. The accuracy with which the physical conditions in these structures can be determined depends directly on the accuracy and completeness of the atomic and molecular data. These laboratory data are used to support the analysis programs of past and current solar observations (e.g., the Orbiting solar Observatories, the Solar Maximum Mission, the Skylab Apollo Telescope Mount, and the Naval Research Laboratory's rocket-borne High Resolution Telescope and Spectrograph). In addition, we attempted to anticipate the needs of future space-borne solar studies such as from the joint ESA/NASA Solar and Heliospheric Observatory (SOHO) spacecraft. Our laboratory activities stressed two categories of study: (1) the measurement of absolute rate coefficients for dielectronic recombination and electron impact excitation; and (2) the measurement of atomic transition probabilities for solar density diagnostics. A brief summary of the research activity is provided.
The discovery of technetium (Tc) in the atmospheres of red giants by Merrill (1952, ApJ, 116, 21) constituted convincing proof that s-process nucleosynthesis is indeed occurring in evolved stars. In principle, Tc should still be present in the atmospheres of hot post-AGB stars and (pre-) white dwarfs although, due to radioactive decay, it should be present in decreasing quantities along post-AGB evolution. The recent discovery of a large number of trans-iron group elements in hot white dwarfs with atomic numbers in the range A = 30-56 (Zn to Ba) raises the prospect that Tc (A = 43) may also be detected. However, this is currently not feasible because no atomic data exist for ionization stages beyond Tc ii. As an initial step, we calculated atomic energy levels and oscillator strengths of Tc iv-vi and used these data to compute non-local thermodynamic equilibrium (NLTE) model atmospheres to estimate at which minimum abundance level Tc could be detected. We show that Tc lines can be found in ultraviolet spectra of hot white dwarfs provided Tc is as abundant as other detected trans-Fe elements. We find that radiative levitation can keep Tc in large, easily detectable quantities in the atmosphere. A direct identification of Tc lines is still not feasible because wavelength positions cannot be computed with necessary precision. Laboratory measurements are necessary to overcome this problem. Our results suggest that such efforts are beneficial to the astrophysical community.
The purpose is to ensure that today’s processing codes produced output to meet today’s accuracy needs. Since 1958 ENDL and about 1965 ENDF have each used a text format to define nuclear and atomic data in 11 columns for each data field. When these formats originated this was judged to be adequate to reproduce the accuracy of data at the time and to meet the needs of our applications. When these formats originated the dominant computer language of the day was FORTRAN and if written using an E11.4 format it would include only 4 or 5 digits of precision, e.g., 0.1234E-03 or 1.2345E-02, varying from one computer/system to another the result was not even unique. In the case of ENDF the 4 digit precision was not even adequate to uniquely define the atomic weight of the target, e.g., U238 = 92238 = 0.9224E+5 = WRONG! From its inceptions the ENDF format had a precision problem. One of my first tasks when in 1967 fresh out of graduate school I joined what later became the National Nuclear Data Center (NNDC), was to address this precision problem. By working with ENDF producers and users throughout the U.S. we verified, 1) E or D is not required to define FORTRAN readable numbers, e.g., E+4 or +4 are both o.k. 2) With ENDF energy eV and cross section in barns, 2 digit exponents are almost never required. 3) Since energy is never negative we could use the first of the 11 columns for a digit. Knowing this allowed us to produce ENDF/B-II to 6 or 7 digit precision, e.g., blank, decimal point, 2 or 3 digit exponent, e.g., ^1.23456-12 or ^1.234567-3. Below is an example of the actual ENDF/B-II data released. Note, the date 1970 and the atomic weight, ZA, uniquely defined to 6-digit accuracy, ^9.22350+ 4.
This annual report covers the period from Oct 1, 2000 to Sep 30, 2001. The Astrophysics Plasma Emission Code and Database (APEC/APED), developed in part under this grant, have been upgraded to Version 1.1 and are now beginning to be used outside our research group in applications to X-ray spectral data from Chandra and XMM-Newton. These models represent the best theoretical data currently available and are reasonably complete below about 30 angstroms. Stellar coronae are being used to benchmark the atomic data in APED as part of the Emission Line Project. Initial results suggest that the models for most of the strongest lines are in good agreement with the observations, including the H-like and He-like emission from dominant elements and the Fe L shell emission, both near 1 keV and in the extreme ultraviolet near 100 eV. At this point in time, we define 'good agreement' at the level of accuracy expected from the atomic physics, approximately 20-30%. In order to benchmark the spectral models beyond the canonical theoretical accuracy, we are working closely with the Chandra gratings calibration group to ensure that we are using the optimal calibration, primarily effective areas and line response functions.
Element abundance ratios of magnesium to neon (Mg/Ne) and neon to oxygen (Ne/O) in the transition region of the quiet Sun have been derived by re-assessing previously published data from the Coronal Diagnostic Spectrometer on board the Solar and Heliospheric Observatory in the light of new atomic data. The quiet Sun Mg/Ne ratio is important for assessing the effect of magnetic activity on the mechanism of the first ionization potential (FIP) effect, while the Ne/O ratio can be used to infer the solar photospheric abundance of neon, which cannot be measured directly. The average Mg/Ne ratio is found to be 0.52+/-0.11, which applies over the temperature region 0.2-0.7MK, and is consistent with the earlier study. The Ne/O ratio is, however, about 40%larger, taking the value 0.24+/-0.05 that applies to the temperature range 0.08-0.40MK. The increase is mostly due to changes in ionization and recombination rates that affect the equilibrium ionization balance. If the Ne/O ratio is interpreted as reflecting the photospheric ratio, then the photospheric neon abundance is 8.08+/-0.09 or 8.15+/-0.10 (on a logarithmic scale for which hydrogen is 12), according to whether the oxygen abundances of M.Asplund et al. or E.Caffau et al. are used. The updated photospheric neon abundance implies a Mg/Ne FIP bias for the quiet Sun of 1.6+/-0.6.
Abstract The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.
Laboratory facilities have made great strides in producing large sets of reliable data for X-ray astronomy, which include ionization and recombination cross sections needed for charge balance calculations as well as the atomic data needed for interpreting X-ray line formation. We discuss data from the new generation sources and pay special attention to the LLNL electron beam ion trap experiment, which is unique in its ability to provide direct laboratory access to spectral data under precisely controlled conditions that simulate those found in many astrophysical plasmas. Examples of spectral data obtained in the 1-160 A wavelength range are given illustrating the type of laboratory X-ray data produced in support of such missions as Chandra, X-Ray Multi-Mirror telescope (XMM), Advanced Satellite for Cosmology and Astrophysics (ASCA) and Extreme Ultraviolet Explorer Satellite (EUVE).