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DOE OSTI2022

Deterministic and Monte Carlo Nuclear Data Adjustment Methods [Slides]

For the Bayesian Monte Carlo methodology, a need to understand convergence of the posterior moments as a function of the number of parameter realizations is required. In high-dimensional systems, it can be very costly to sample entire parameter space and perform functional evaluation for every realization. Bayesian Monte Carlo allows one to relax the GLLS approximations of model linearity and prior/posterior PDF shape. The Bayesian Stochastic Collocation Method is a deterministic approach to “sample” the parameter space. It allows one to relax the GLLS approximations of model linearity and posterior PDF shape. Higher-order posterior moments (i.e., skewness, kurtosis, etc.) can be studied through polynomial expansion. Tensor product quadrature scales poorly and can use sparse grid quadrature methods.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

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 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↗

NASA NTRS1995

Disruption of the Globular Cluster Pal 5

Orbit calculations suggest that the sparse globular cluster, Pal 5, will pass within 7 kpc of the Galactic center the next time it crosses the plane, where it might be destroyed by tidal stresses. We study this problem, treating Pal 5 as a self-consistent dynamical system orbiting through an external potential that represents the Galaxy. The first part of the problem is to find suitable analytic approximations to the Galactic potential. They must be valid in all regions the cluster is likely to explore. Observed velocity and positional data for Pal 5 are used as initial conditions to determine the orbit. Methods we used for a different problem some 12 years ago have been adapted to this problem. Three experiments have been run, with M/L= 1, 3, and 10, for the cluster model. The cluster blew up shortly after passing through the Galactic plane (about 130 Myrs after the beginning of the run) with M/L=1. At M/L = 3 and 10 the cluster survived, although it got quite a kick in the fundamental mode on passing through the plane. But the fundamental mode oscillation died out in a couple of oscillation cycles at M/L=10. Pal 5 will probably be destroyed on its next crossing of the Galactic plane if M/L=1, but it can survive (albeit with fairly heavy damage) if NI/L=3. We haven't tried to trap the mass limits more closely than that. Pal 5 comes through pretty well unscathed at M/L=10. An interesting follow-up experiment would be to back the cluster up along its orbit to look at its previous passage through the Galactic plane, to see what kind of object it might have been at earlier times.

Miller, R. H.↗

NASA NTRS2007

An EOF Iteration Approach for Obtaining Homogeneous Radiative Fluxes from Satellites Observations

Conventional observations of climate parameters are sparse in space and/or in time and the representativeness of such information needs to be optimized. Observations from satellites provide improved spatial coverage than point observations however they pose new challenges for obtaining homogeneous coverage. Surface radiative fluxes, the forcing functions of the hydrologic cycle and biogeophysical processes, are now becoming available from global scale satellite observations. They are derived from independent satellite platforms and sensors that differ in temporal and spatial resolution and in the size of the footprint from which information is derived. Data gaps, degraded spatial resolution near boundaries of geostationary satellites, and different viewing geometries in areas of satellite overlap, could result in biased estimates of radiative fluxes. In this study, discussed will be issues related to the sources of inhomogeneity in surface radiative fluxes as derived from satellites; development of an approach to obtain homogeneous data sets; and application of the methodology to the widely used International Satellite Cloud Climatology Project (ISCCP) data that currently serve as a source of information for deriving estimates of surface and top of the atmosphere radiative fluxes. Introduced is an Empirical Orthogonal Function (EOF) iteration scheme for homogenizing the fluxes. The scheme is evaluated in several ways including comparison of the inferred radiative fluxes against ground observations, both before and after the EOF approach is applied. On the average, the latter reduces the rms error by about 2-3 W/m2.

Zhang, Banglin↗

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 OSTI2024

Simulations of Sparse Static Detector Networks for City-Scale Radiological/Nuclear Detection

Sparse static detector networks in urban environments can be used in efforts to detect illicit radioactive sources, such as stolen nuclear material or radioactive "dirty bombs." We use detailed simulations to evaluate multiple configurations of detector networks and their ability to detect sources moving through a $6\times 6$ km 2 area of downtown Chicago. A detector network's probability of detecting a source increases with detector density but can also be increased with strategic node placement. Here, we show that the ability to fuse correlated data from a source-carrying vehicle passing by multiple detectors can significantly contribute to the overall detection probability. In this article, we distinguish static sensor deployments operated as networks able to correlate signals between sensors, from deployments operated as arrays where each sensor is operated individually. In particular, we show that additional visual attributes of source-carrying vehicles, such as vehicle color and make, can greatly improve the ability of a detector network to detect illicit sources.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2021

Electrochemistry-Induced Restructuring of Tin-Doped Indium Oxide Nanocrystal Films of Relevance to CO 2 Reduction

The electrochemical reduction of CO 2 into fuels using renewable electricity presents an opportunity to utilize captured CO 2 . Electrocatalyst development has been a primary focus of research in this area. This is especially true at the nanoscale, where researchers have focused on understanding nanostructure-property relationships. However, electrocatalyst structure may evolve during operation. Indium- and tin-based oxides have been widely studied as electrocatalysts for CO 2 reduction to formate, but evolution of these catalysts during operation is not well-characterized. Here, we report the evolution of nanoscale structure of precise tin-doped indium oxide nanocrystals under CO 2 reduction conditions. We show that sparse monolayer nanocrystal films desorb from the electrode upon charging, but thicker nanocrystal films remain, likely due to an increased number of physical contacts. Upon applying a cathodic voltage of –1.0 V vs RHE or greater, the original 10-nm diameter nanocrystals are no longer visible, and instead form a larger microstructural network. Elemental analysis suggests the network is an oxygen-deficient indium-tin metal alloy. We hypothesize that this morphological evolution is the result of nanocrystal sintering due to oxide reduction. Furthermore, these data provide insights into the morphological evolution of tin-doped indium oxide nanocrystal electrocatalysts under reducing conditions and highlight the importance of post-electrochemical structural characterization of electrocatalysts.

25 ENERGY STORAGE↗

NASA NTRS1990

Estimation of multidimensional precipitation parameters by areal estimates of oceanic rainfall

The parameters of the multidimensional precipitation model proposed by Waymire et al. (1984) are estimated using the areal-averaged radar measurements of precipitation of the Global Atlantic Tropical Experiment (GATE) data set. The procedure followed was the fitting of the first- and second-order moments at different aggregation scales by nonlinear regression techniques. The numerical estimates of the parameters using different subsets of GATE information were reasonably stable, i.e., they were not affected by changes of the area-averaging size, temporal length of the records, and percentage of areal coverage of rainfall. This suggests that the estimation procedure is relatively robust and suitable to estimate the parameters of the multidimensional model in areas of sparse density of rain gages. The use of the space-time spectrum of rainfall to help in the determination of sampling errors due to intermittent visits of future space-borne low-altitude sensors of precipitation is also discussed.

Valdes, J. B.↗

NASA NTRS2000

Parallel Conjugate Gradient: Effects of Ordering Strategies, Programming Paradigms, and Architectural Platforms

The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. A sparse matrix-vector multiply (SPMV) usually accounts for most of the floating-point operations within a CG iteration. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and SPMV using different programming paradigms and architectures. Results show that for this class of applications, ordering significantly improves overall performance, that cache reuse may be more important than reducing communication, and that it is possible to achieve message passing performance using shared memory constructs through careful data ordering and distribution. However, a multi-threaded implementation of CG on the Tera MTA does not require special ordering or partitioning to obtain high efficiency and scalability.

Oliker, Leonid↗

DOE OSTI2022

Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties

Abstract Identification of a heterogeneous conductivity field and reconstruction of a contaminant release history are key aspects of subsurface remediation. These two goals are achieved by combining model predictions with sparse and noisy hydraulic head and concentration measurements. Solution of this inverse problem is notoriously difficult due to, in part, high dimensionality of the parameter space and high computational cost of repeated forward solves. We use a convolutional adversarial autoencoder (CAAE) to parameterize a heterogeneous non‐Gaussian conductivity field via a low‐dimensional latent representation. A three‐dimensional dense convolutional encoder‐decoder (DenseED) network serves as a forward surrogate of the flow and transport model. The CAAE‐DenseED surrogate is fed into the ensemble smoother with multiple data assimilation (ESMDA) algorithm to sample from the Bayesian posterior distribution of the unknown parameters, forming a CAAE‐DenseED‐ESMDA inversion framework. The resulting CAAE‐DenseED‐ESMDA inversion strategy is used to identify a three‐dimensional contaminant source and conductivity field. A comparison of the inversion results from CAAE‐ESMDA with physical flow and transport simulator and from CAAE‐DenseED‐ESMDA shows that the latter yields accurate reconstruction results at the fraction of the computational cost of the former.

Zhou, Zitong↗

DOE OSTI2025

AmeriFlux CA-LU2 Lutose

This is the AmeriFlux version of the carbon flux data for the site CA-LU2 Lutose. Site Description - Lutose is a peat plateau that burned from a moderate forest fire in June 2007. Prior to the fire the site was likley an open canopy of stunted black spruce and a ground layer of Labrador tea shurbs and lichen or sphagnum. No black spruce survived the fire and lichens were still completely absent during this study. By 2019, most charred tree boles has fallen over and vegetation recovery was dominated by dense Labrador tea shrubs and sparse regenerating black spruce with around >150cm of peat.

Schulze, Christopher [University of Alberta]↗

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

Measuring Electron Diffusion and Constraining the Neutral Current $\pi^0$ Background for Single-Photon Events in MicroBooNE

Liquid Argon Time Projection Chambers (LArTPCs) are a rising technology in the field of experimental neutrino physics. LArTPCs use ionization electrons and scintillation light to reconstruct neutrino interactions with exceptional calorimetric and position resolution capabilities. Here, I present two analyses conducted in the MicroBooNE LArTPC at Fermilab: a measurement of the longitudinal electron diffusion coefficient, $D_L$, in the MicroBooNE detector and a constraint of the systematic uncertainty on MicroBooNE's single-photon analysis due to the dominant neutral current (NC) \piz{} background. Longitudinal electron diffusion modifies the spatial and timing resolution of the detector, and measuring it will help correct for these effects. Furthermore, current measurements of $D_L$ in liquid argon are sparse and in tension with one another, making the MicroBooNE measurement especially valuable. We report a measurement of $3.74^{+0.28}_{-0.29}$ cm$^2$/s. MicroBooNE is searchin g for si ngle-photon events as a potential explanation for the MiniBooNE low-energy excess (LEE) of electron neutrino-like events, which has been interpreted as evidence for low-mass sterile neutrinos. However, this search is overwhelmed by a large NC \piz{} background. By performing a sideband selection of NC \piz{} events, we apply a data-driven rate constraint to the single-photon analysis to reduce the systematic uncertainties. At present, this constraint improves the single-photon analysis' median sensitivity to the LEE-like signal from 0.9$\sigma$ to $1.5\sigma$. This sensitivity is expected to improve significantly as more data become available. Both of these measurements will not only benefit MicroBooNE, but also inform future LArTPC experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Explosion Detection Using Smartphones: Ensemble Learning with the Smartphone High-Explosive Audio Recordings Dataset and the ESC-50 Dataset

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially at the local scale, as maintaining and deploying networks is costly. With increasing interest in smaller-yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosive Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either “explosion”, “ambient”, or “other” with true positive rates (recall) greater than 96% for all three categories.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

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 OSTI2022

An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services

Many transit agencies operating paratransit and microtransit services have to respond to trip requests that arrive in real-time, which entails solving hard combinatorial and sequential decision-making problems under uncertainty. To avoid decisions that lead to significant inefficiency in the long term, vehicles should be allocated to requests by optimizing a non-myopic utility function or by batching requests together and optimizing a myopic utility function. While the former approach is typically offline, the latter can be performed online. We point out two major issues with such approaches when applied to paratransit services in practice. First, it is difficult to batch paratransit requests together as they are temporally sparse. Second, the environment in which transit agencies operate changes dynamically (e.g., traffic conditions can change over time), causing the estimates that are learned offline to become stale. To address these challenges, we propose a fully online approach to solve the dynamic vehicle routing problem (DVRP) with time windows and stochastic trip requests that is robust to changing environmental dynamics by construction. We focus on scenarios where requests are relatively sparse—our problem is motivated by applications to paratransit services. We formulate DVRP as a Markov decision process and use Monte Carlo tree search to evaluate actions for any given state. Accounting for stochastic requests while optimizing a non-myopic utility function is computationally challenging; indeed, the action space for such a problem is intractably large in practice. To tackle the large action space, we leverage the structure of the problem to design heuristics that can sample promising actions for the tree search. Our experiments using real-world data from our partner agency show that the proposed approach outperforms existing state-of-the-art approaches both in terms of performance and robustness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗