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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

pyDRESCALk

Modern data scientists are tasked to analyze ever-growing data sets with increasingly complex relationships. Tensor decompositions have come to play a central role in identifying underlying latent structures in higher-order data. The problem of fitting tensor models to different distributions is complicated by the combinations of size, dimensionality, and sparsity present in real world data. The situation demands efficient algorithms designed for shared-memory and distributed systems. This work will present new research that tackles these challenges on several different fronts, leveraging optimizations in numerical algorithms and sparse tensor representations in heterogeneous high performance computing environments.

Bhattarai, Manish↗
DOE OSTI2022

Automated identification of local contamination in remote atmospheric composition time series

Abstract. Atmospheric observations in remote locations offer a possibility of exploring trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this contamination is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local contamination in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with a low background signal compared to the contaminated signal. The pollution detection algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the derivative (time derivative) of a concentration over time. If this derivative exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median, and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be “tuned” to be more or less stringent (e.g., flag more or fewer data points as contaminated). The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. Using strict settings, we identified 62 % of the data as influenced by local contamination. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied to a trace gas dataset (CO2), also collected during MOSAiC, and to another particle number concentration dataset, collected at the high-altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local contamination in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open-access code enables reproducible application to a wide suite of different datasets. It is available at https://doi.org/10.5281/zenodo.5761101 (Beck et al., 2021).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Sparse Data Management in HDF5

The importance of sparse data management is growing with data produced by large-scale experimental and observational facilities that contain small amounts of non-zero values. In this document, we explore different design options to support sparse data in HDF5, one of the most popular highperformance I/O libraries and file formats used for scientific data. We discuss several use cases and requirements. Our main hard design constraint is that any changes to the HDF5 dataset API would be a burden on users and not acceptable. The remaining options are discussed below. We provide rationale for what we believe is the strongest candidate and describe how its potential benefits can be simulated with the existing HDF5 library. We have conducted a set of computational experiments the results of which are reported here. They show that our candidate meets all relevant requirements and gives us a certain degree of confidence for an HDF5 library-native implementation.

Mainzer, John↗
DOE OSTI2023

Lyman continuum leaker candidates among highly ionised, low-redshift dwarf galaxies selected from He II

Contemporary research suggests that the reionisation of the intergalactic medium (IGM) in the early Universe was predominantly realised by star-forming (proto-)galaxies (SFGs). Due to observational constraints, our knowledge on the origins of sufficient amounts of ionising Lyman continuum (LyC) photons and the mechanisms facilitating their transport into the IGM remains sparse. Recent efforts have thus focussed on the study of local analogues to these high-redshift objects. We aim to acquire a set of very low-redshift SFGs that exhibit signs of a hard radiation field being present. A subsequent analysis of their emission line properties is intended to shed light on how the conditions prevalent in these objects compare to those predicted to be present in early SFGs that are thought to be LyC emitters (LCEs). We used archival spectroscopic SDSS DR12 data to select a sample of low-redshift He II 4686 emitters and restricted it to a set of SFGs with an emission line diagnostic sensitive to the presence of an active galactic nucleus, which serves as our only selection criterion. We performed a population spectral synthesis with FADO to reconstruct these galaxies’ star-formation histories (SFHs). Utilising the spectroscopic information at hand, we constrained the predominant ionisation mechanisms in these galaxies and inferred information on interstellar medium (ISM) conditions relevant for the escape of LyC radiation. Our final sample consists of eighteen ionised, metal-poor galaxies (IMPs). These low-mass (6.2 ≤ log(M * /M ⊙ ) ≤ 8.8), low-metallicity (7.54 ≤log(O/H) + 12 ≤ 8.13) dwarf galaxies appear to be predominantly ionised by stellar sources. We find large [OIII] 5007/[OII] 3727 ratios and [SII] 6717,6731/Hα deficiencies, which provide strong indications for these galaxies to be LCEs. At least 40% of these objects are candidates for featuring cosmologically significant LyC escape fractions ≳10%. The IMPs’ SFHs exhibit strong similarities and almost all galaxies appear to contain an old (> 1 Gyr) stellar component, while also harbouring a young, two-stage (~10 Myr and < 1 Myr) starburst, which we speculate might be related to LyC escape. The properties of the compact emission line galaxies presented here align well with those observed in many local LCEs. In fact, our sample may prove as an extension to the rather small catalogue of local LCEs, as the extreme ISM conditions we find are assumed to facilitate LyC leakage. Notably, all of our eighteen candidates are significantly closer (z < 0.1) than most established LCEs. If the inferred LyC photon loss is genuine, this demonstrates that selecting SFGs from He II 4686 is a powerful selection criterion in the search for LCEs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Influence of Vertical Hydrologic Exchange Flow, Channel Flow, and Biogeochemical Kinetics on CH 4 Emissions From Rivers

Abstract CH 4 emissions from inland water are highly uncertain in the current global CH 4 budget, especially for rivers and streams due to sparse measurements and the uncertainty of measurements caused by turbulent water flow. A previous study has revealed that vertical hydrologic exchange flow (VHEF) is the main regulator of CH 4 emissions from riverbed sediments. However, to what extent the understanding obtained from the plot‐scale can be extended to the reach scale and basin scale remains unknown. To address this challenge, we developed a process‐based model to estimate CH 4 flux at the air‐water interface using the attributes available in the national hydrography data set. It calculates the annual mean flux of VHEF, CH 4 production in sediments, and CH 4 transport in the river channel in a sequential manner. Model performance is evaluated by CH 4 efflux observed at the Hanford reach of the Columbia River. We show that reach‐wise sediment hydrologic and biogeochemical conditions estimated from the national hydrography data set could serve as a good indicator of CH 4 emissions from rivers. Aerobic methane oxidation and export to the downstream are the dominant ways of total CH 4 loss for the large lowland river. The hotspots of CH 4 emissions are likely to be at the reaches with fine sediments and slow channel velocity. This study demonstrates the possibility of quantifying CH 4 emissions at the reach scale and the modeling framework has the potential to be extended to the basin scale to improve estimates of CH 4 emissions from lotic inland water.

58 GEOSCIENCES↗
DOE OSTI2018

Uncovering anisotropic magnetic phases via fast dimensionality analysis

A quantitative geometric predictor for the dimensionality of magnetic interactions is presented. This predictor is based on networks of superexchange interactions and can be quickly calculated for crystalline compounds of arbitrary chemistry, occupancy, or symmetry. The resulting data are useful for classifying structural families of magnetic compounds. We have examined compounds from a demonstration set of 42,520 materials with 3d transition metal cations. The predictor reveals trends in magnetic interactions that are often not apparent from the space group of the compounds, such as triclinic or monoclinic compounds that are strongly 2D. We present speci c cases where the predictor identi es compounds that should exhibit competition between 1D and 2D interactions, and how the predictor can be used to identify sparsely-populated regions of chemical space with as-yet-unexplored topologies of speci c 3d magnetic cations. The predictor can be accessed for the full list of compounds using a searchable frontend, and further information on the connectivity, symmetry, valence, and cation-anion and cation-cation coordination can be freely exported.

36 MATERIALS SCIENCE↗
DOE OSTI2022

PyAMG: Algebraic Multigrid Solvers in Python

PyAMG is a Python package of algebraic multigrid (AMG) solvers and supporting tools for approximating the solution to large, sparse linear systems of algebraic equations, Ax = b, where A is an n × n sparse matrix. Sparse linear systems arise in a range of problems in science, from fluid flows to solid mechanics to data analysis. While the direct solvers available in SciPy’s sparse linear algebra package (scipy.sparse.linalg) are highly efficient, in many cases iterative methods are preferred due to overall complexity. However, the iterative methods in SciPy, such as CG and GMRES, often require an efficient preconditioner in order to achieve a lower complexity. Preconditioning is a powerful tool whereby the conditioning of the linear system and convergence rate of the iterative method are both dramatically improved. PyAMG constructs multigrid solvers for use as a preconditioner in this setting. A summary of multigrid and algebraic multigrid solvers can be found in Olson (2015a), in Olson (2015b), and in Falgout (2006); a detailed description can be found in Briggs et al. (2000) and Trottenberg et al. (2001).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

47 Tuc in Rubin Data Preview 1. Exploring Early LSST Data and Science Potential

We present analyses of the early data from Rubin Observatory’s Data Preview 1 (DP1) for the field of the globular cluster 47 Tuc. The DP1 data set for 47 Tuc includes four nights of observations from the Rubin Commissioning Camera (LSSTComCam), covering multiple bands (ugriy). We address challenges of crowding in the inner region of the cluster and toward the SMC in DP1, and demonstrate improved star–galaxy separation by fitting fifth-degree polynomials to the stellar loci in color–color diagrams and applying multidimensional sigma clipping. We compile a catalog of 3576 probable 47 Tuc member stars selected via a combination of isochrone, Gaia proper-motion, and color–color space matched filtering. We explore the sources of photometric scatter in the 47 Tuc color–color sequence, evaluating contributions from various potential sources, including differential extinction within the cluster. Finally, of the 72 well-characterized variables in the field, we recover three known variable stars, including two RR Lyrae and one eclipsing binary, in the coadd-based object catalog, and identify 62 in the difference image-based object catalog. Although the DP1 lightcurves have sparse temporal sampling, they appear to follow the patterns of densely sampled literature lightcurves well. Despite some data limitations for crowded-field stellar analysis, DP1 demonstrates the promising scientific potential for future LSST data releases.

Choi, Yumi [NSF National Optical-Infrared Astronom↗
DOE OSTI2021

Phase-weighted slant stacking for surface wave dispersion measurement

SUMMARY Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals’ acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms’ domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.

58 GEOSCIENCES↗
DOE OSTI2023

$S$ Hmax orientation in the Alpine region from observations of stress-induced anisotropy of nonlinear elasticity

The orientation of $S$ Hmax is commonly estimated from in situ borehole breakouts and earthquake focal mechanisms. Borehole measurements are expensive, and therefore sparse, and earthquake measurements can only be made in regions with many well-characterized earthquakes. Here, we derive the stress-field orientation using stress-induced anisotropy in nonlinear elasticity. In this method, we measure the strain derivative of velocity as a function of azimuth. We use a natural pump-probe (NPP) approach which consists of measuring elastic wave speed using empirical Green’s functions (probe) at different points of the earth tidal strain cycle (pump). The approach is validated using a larger data set in the Northern Alpine Foreland region where the orientation of maximum horizontal compressive stress is known from borehole breakouts and drilling-induced fractures. The technique resolves NNW-SSW to N-S directed $S$ Hmax which is in good agreement with conventional methods and the recent crustal stress model. We confirm that the NPP method can be applied to dense large-scale seismic arrays. The technique is then applied to the Southern Alps to understand the contemporary stress pattern associated with the ongoing deformation due to counterclockwise rotation of the Adriatic plate with respect to the European plate. Our results explain why the two major faults in Northeastern Italy, the Giudicarie Fault and the Periadriatic Line (Pustertal–Gailtal Fault) are currently inactive, while the currently acting stress field allows faults in Slovenia to deform actively. We have demonstrated that the pump-probe method has the potential to fill in the measurement gap left by conventional approaches, both in terms of regional coverage and in depth.

58 GEOSCIENCES↗
DOE OSTI2023

Group Projected subspace pursuit for IDENTification of variable coefficient differential equations (GP-IDENT)

We propose an effective and robust algorithm for identifying partial differential equations (PDEs) with space-time varying coefficients from the noisy observation of a single solution trajectory. Identifying unknown differential equations from noisy data is a difficult task, and it is even more challenging with space and time varying coefficients in the PDE. The proposed algorithm, GP-IDENT, has three ingredients: (i) we use B-spline bases to express the unknown space and time varying coefficients, (ii) we propose Group Projected Subspace Pursuit (GPSP) to find a sequence of candidate PDEs with various levels of complexity, and (iii) we propose a new criterion for model selection using the Reduction in Residual (RR) to choose an optimal one among a pool of candidates. The new GPSP considers group projected subspaces which is more robust than existing methods in distinguishing correlated group features. We test GP-IDENT on a variety of PDEs and PDE systems, and compare it with the state-of-the-art parametric PDE identification algorithms under different settings to illustrate its outstanding performance. Furthermore, our experiments show that GP-IDENT is effective in identifying the correct terms from a large dictionary, and our model selection scheme is robust to noise.

Data-driven method↗
DOE OSTI2020

Topological Analysis of The SPOKE Graph

The SPOKE graph [2, 6] is a sparse decorated semantic graph representing a collection of knowledge collected in many scientific databases from the fields of healthcare, biochemistry, chemistry, biology, et cetera. This knowledge graph is stored as a relational dataset decorated with metadata on each constituent vertex and edge. Formally, the graph is G(V, E, D), where V is a set of n vertices V := {1, ..., n} and edges of the form (i, j) ϵ E for i, j ϵ V, and table D that for any item in V υ E stores unstructured data such as vertex/edge type, nature of a relationship, et cetera. D(i) = {data involving vertex i ϵ V}, and D(i, j) = {data involving edge (i, j) ϵ E}. Here, we treat the graph as undirected in the sense that a direct relationship for (i, j) causes a (possibly opposite) reverse direct relationship for (j, i). The SPOKE graph G(V, E, D) is formed by processing a collection of relational datasets from medicine, chemistry, and biology, connecting many entities. Here, we analyze an instance from 2019, Spoke-20190707, where a graph file contains 6.16M edges and associated metadata and a vertex file contains 2.15M vertices and the associated metadata. There are 12 different types of vertex entities; all edge types used are implicit (see §2). There is other metadata in D on edges and vertices, but we just use the topology and the vertex labels in this report. SPOKE is growing as more knowledge is gained and more datasets are added. SPOKE is likely to grow 10x during the next phase of this project, and we therefore would like to consider topoligical analysis techniques that are scalable to several orders of magnitude larger than the current dataset (say >1B edges).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

EXPRES. II. Searching for Planets around Active Stars: A Case Study of HD 101501

By controlling instrumental errors to below 10 cm s{sup −1}, the EXtreme PREcision Spectrograph (EXPRES) allows for a more insightful study of photospheric velocities that can mask weak Keplerian signals. Gaussian processes (GP) have become a standard tool for modeling correlated noise in radial velocity data sets. While GPs are constrained and motivated by physical properties of the star, in some cases they are still flexible enough to absorb unresolved Keplerian signals. We apply GP regression to EXPRES radial velocity measurements of the 3.5 Gyr old chromospherically active Sun-like star, HD 101501. We obtain tight constraints on the stellar rotation period and the evolution of spot distributions using 28 seasons of ground-based photometry, as well as recent Transiting Exoplanet Survey Satellite data. Light-curve inversion was carried out on both photometry data sets to reveal the spot distribution and spot evolution timescales on the star. We find that the >5 m s{sup −1} rms radial velocity variations in HD 101501 are well modeled with a GP stellar activity model without planets, yielding a residual rms scatter of 45 cm s{sup −1}. We carry out simulations, injecting and recovering signals with the GP framework, to demonstrate that high-cadence observations are required to use GPs most efficiently to detect low-mass planets around active stars like HD 101501. Sparse sampling prevents GPs from learning the correlated noise structure and can allow it to absorb prospective Keplerian signals. We quantify the moderate to high-cadence monitoring that provides the necessary information to disentangle photospheric features using GPs and to detect planets around active stars.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Reduced Order Model to Predict Dispersion of Flammable Refrigerant into a Space

As the HVAC&R industry mobilizes to deploy more low-GWP refrigerants, relevant standards are being continually reviewed and updated. Those include the general safety standards ISO 5149 and ASHRAE 15, and the equipment standards IEC and UL. The standards systematically set the allowable maximum amount of refrigerant that should be used in different equipment types and different applications. To do so, they rely on predictions of how a leaked refrigerant mass will disperse into a space. Dispersion characteristics, such as total flammable volume and its residence time, determine the risk associated with the presence of the flammable refrigerant. The standards have included provisions for the use of flammable refrigerants for approximately two decades. They relied on limited analytical analyses and test cases in their development. Dispersion of a refrigerant into a space is complex. Computational fluid dynamics (CFD) are the most accurate in predicting a given problem. However, CFD is computationally expensive and requires specialized expertise and resources and is not suitable for use by standards development working group as prediction tool. This paper presents the development of a reduced order model (ROM) that predicts the key dispersion characteristics relevant to the dispersion of a leaked refrigerant into a space for any combination of input variables. The inputs are the refrigerant release height, the total released refrigerant mass and its release flow rate, the refrigerant molecular weight, the ventilation flow rate, the floor area and height of the space, recirculation air flow rate, and the tightness of the space. The outputs are histograms of volume fraction of the room in prescribed concentration bins and the total mass of the refrigerant in each bin normalized by the total refrigerant charge at 13 prescribed simulation time stamps between 1 and 900 seconds. The ROM is constructed from a set of CFD simulations with carefully chosen combinations of input parameters. The selection if done using a multidimensional sparse grid which is a generalization of the classical tensor approach but offers additional flexibility and thus can be more carefully tuned towards a specific model. The tuning is done to improve the accuracy, measured in the difference between the output values of the ROM and the CFD model, while minimizing the computational cost, measured in number of CFD simulations which is orders of magnitude more expensive than the processing the training data.

Edwards, Dean↗
DOE OSTI2026

Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra

Identifying molecular structure based on spectroscopic readings is a key task in a variety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless require expert-level knowledge to decode. Machine learning has emerged as a potential solution for automating structure prediction from chemical spectra; however, current approaches generally focus on single sensor modalities, neglecting to leverage the complementary information contained within differing spectra. In this paper, we introduce Peak2Patch, a novel approach to fusion-enhanced prediction of functional groups from IR and mass spectra. First, we perform a detailed comparison of backbone networks for encoding both sparse mass spectra and dense IR spectra and demonstrate the superior performance of transformer neural networks over current state-of-the-art convolutional neural networks. Second, we evaluate three broad categories of fusion: early (raw feature), middle (deep feature), and late (decision) fusion, demonstrating the potential of a deep feature fusion-based approach. Lastly, we present Peak2Patch, our attention-based fusion scheme, which leverages cross-attention to mix features between encoded tokens of the two modalities. We validate our approach on a publicly available multimodal spectroscopic data set of 790k simulated molecules, demonstrating a large improvement in functional group prediction over both the previous state-of-the-art and our own strong single-modal baselines.

Jacobson, Philip [Sandia National Laboratories (SN↗
DOE OSTI2021

Learning an Algebriac Multrigrid Interpolation Operator Using a Modified GraphNet Architecture

This work, building on previous efforts, develops a suite of new graph neural network machine learning architectures that generate data-driven prolongators for use in Algebraic Multigrid (AMG). Algebraic Multigrid is a powerful and common technique for solving large, sparse linear systems. Its effectiveness is problem dependent and heavily depends on the choice of the prolongation operator, which interpolates the coarse mesh results onto a finer mesh. Previous work has used recent developments in graph neural networks to learn a prolongation operator from a given coefficient matrix. In this paper, we expand on previous work by exploring architectural enhancements of graph neural networks. A new method for generating a training set is developed which more closely aligns to the test set. Asymptotic error reduction factors are compared on a test suite of 3-dimensional Poisson problems with varying degrees of element stretching. Results show modest improvements in asymptotic error factor over both commonly chosen baselines and learning methods from previous work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Building Alternative Indices of Socioeconomic Status for Population Modeling in Data-Sparse Contexts (Short Paper)

Population modeling requires clear definitions of socioeconomic status (SES) to ensure overall estimate accuracy and locate potentially underserved subpopulations. This presents a challenge as SES can be measured in myriad ways and for divergent purposes, and the data required to calculate these metrics may be lacking, particularly in low and middle income countries (LMICs). To support more refined SES measurement, we explore improvements upon the Demographic and Health Survey’s (DHS) Wealth Index (DHS-WI) using alternative characterizations of SES based on multiple correspondence analysis (MCA) and hierarchical clustering. We produce the MCA-derived metrics first on a full suite of household economic, demographic, and dwelling variables, then on a reduced set of occupant-only SES characteristics. We explore the utility of these metrics relative to DHS-WI based on their ability to 1) differentiate DHS household types and 2) identify mixtures of SES levels within DHS samples and mapped at high spatial resolution. We find that our full suite MCA yields more clearly defined SES segments and that our reduced MCA delineates occupant SES most clearly, suggesting potential pathways to improve upon the DHS-WI in future population modeling efforts for LMICs.

Cunningham, Angela↗
DOE OSTI2025

X-ray Computed Tomography Data of Dense Metallic Components

The data shared in here are X-ray computed tomography (XCT) scans of a hexagonal fuel nozzle in 3 sections with the Metrotom 800 system at the Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory. The data are used in the paper "Tomographic Sparse View Selection using the View Covariance Loss, by Lin et al. (doi:10.1109/TPAMI.2025.36000720), accepted to the international conference on computational imaging (ICCP 2025). Figures 4-7 in the paper describe the part/XCT scan. File name Descriptions: Bottom section: TCR- Single Channeled SRC L 2019-3-18 12-26-41.hdf5 Medium section: TCR- Single Channeled SRC M 2019-3-18 13-8-9.hdf5 Top section: TCR- Single Channeled SRC T 2019-3-18 13-45-39.hdf5 Each hdf5 file contains projection data, and all the relevant X-ray CT scan setting. The full list of included attributes: distance_unit: Units of all distances specified angle_unit : Units of the angles angles: Array of all angles used voxel_size_xy: Baseline recon (if any) has this voxel size in the in-plane direction voxel_size_z: Baseline recon (if any) has this voxel size in the cross-plane direction det_pixel_size_col: Size of the detector pixels in the column dimension det_pixel_size_row: Size of the detector pixels in the row dimension src_iso_dist: Source to iso-center distance iso_det_dist: Iso-center to detector distance det_angle: If the detector is rotated/tilted, this angle corresponds to that value det_row_offset: Center of rotation offset in the vertical direction det_col_offset: Center of rotation offset in the horizontal direction reconstruction: A baseline reconstruction stored as 3D array BHC params: Beam-hardening parameters - Van De Casteel Model - if it has been used to pre-process the projections We also provided a python script (hdf_io.py) that allows the user to read the relevant data from each hdf5 file.

Ziabari, Amir [Oak Ridge National Laboratory]↗