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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Atmospheric Modeling and Denoising for Millimeter-Wave Line Intensity Mapping

Line-intensity mapping (LIM) offers a promising approach to mapping large-scale cosmic structure, and the greatest obstacle for ground-based observations at millimeter wavelengths is foreground contamination from atmospheric emission. In this work, we present a simulation and denoising framework designed to isolate and subtract atmospheric fluctuations from LIM data, modeled after the instrument parameters of the South Pole Telescope Summertime Line Intensity Mapper (SPT-SLIM). We generate mock observations spanning 125-175 GHz containing cosmic signals, precipitable water vapor screens, ice crystal fluctuations, and photon noise. We then implement a spatial-spectral atmospheric removal pipeline combining per-pixel linear template regression with a two-dimensional Fourier-domain filter. The framework is evaluated under simulated conditions in the South Pole and the Atacama Desert across three key metrics: cosmic signal preservation, foreground subtraction efficiency, and instrument noise injection. Our pipeline achieves atmospheric suppression at large spatial scales, and these results establish a physically grounded foundation for atmosphere removal in ground-based LIM data collection.

Saye, L. K. [UC, Berkeley (main)] (ORCID:000900078↗
DOE OSTI2020

The Cost of Floating Offshore Wind Energy in California Between 2019 and 2032

California’s energy planning is centered around meeting the emissions reduction and renewable energy requirements of Senate Bill 350 by 2030. However, state power system planning is expected to eventually address California’s requirement to achieve 100% of total retail electricity sales from renewable energy and zero-carbon resources by 2045, as mandated by Senate Bill 100. To comply with these directives, California needs to investigate the further development of energy efficiency, storage, and a diverse range of renewable energy, zero-carbon emission, and transmission resources, including offshore wind. Wind resources off the coast of California have the potential to generate a significant portion of the state’s electric energy as it moves toward a zero-carbon economy and can help diversify its energy mix. Floating offshore wind technology, which is suitable for the deep waters along the California coast, is currently in a precommercial phase, with approximately 84 megawatts (MW) installed worldwide at the end of 2019. Globally there are over 7,000 MW in planning and permitting phases of development, with the first commercial-scale projects expected to be operational in 2024. This study provides site-specific cost and performance data for floating offshore wind to inform California’s long-term energy planning. The identification of new resources to meet California’s policy goals at least cost is part of the Integrated Resource Planning (IRP) process, which is coordinated by the California Public Utilities Commission (CPUC). In 2019–2020 IRP modeling, offshore wind was included for the first time as a candidate resource in some sensitivity cases (CPUC 2019). The data and information presented in this report can be used to update offshore wind inputs in future IRP cycles. The authors conducted a geospatial cost analysis over portions of the offshore wind resource area of California. The analyzed spatial domain includes sites with a mean wind speed of at least 7 meters per second and water depths between 40 meters (m) and 1,300 m. Costs and energy production vary across this analysis domain. We calculated these parameters on a grid layout with over 750 sites, with each site representing a 1,000-MW commercial offshore wind power plant. Levelized cost of energy (LCOE) was calculated at each site over the analysis domain. The resulting variation in LCOE across the analysis domain is illustrated through heat maps in this report. Five study areas were selected within the analysis domain where more detailed cost analysis was conducted and cost parameters, such as annual energy production, capital cost expenditures (CapEx), operational cost expenditures (OpEx), and net capacity factors are reported. These five study areas include Morro Bay, Diablo Canyon, Humboldt, Cape Mendocino and Del Norte.

17 WIND ENERGY↗
DOE OSTI2026

A two-stage optical fusion framework for wildfire severity mapping across the conterminous United States

Accurate wildfire severity mapping (WSM) is essential for post-fire recovery planning, erosion risk assessment, ecosystem monitoring, and disaster risk reduction. Although Landsat and Sentinel optical imagery have been widely used for burn severity assessment, the added value of fusing multiple optical sensors has not been sufficiently quantified across diverse fire events, particularly since the launch of Landsat-9. This study evaluates whether multisensor optical fusion improves wildfire severity mapping relative to single-sensor baselines using Sentinel-2, Landsat-8, and Landsat-9 imagery across 40 wildfire events in the conterminous United States. We tested a two-stage fusion framework that combines feature-level fusion with pixel-level dimensionality reduction. First, feature-level fused datasets were created through early fusion by combining standardized post-fire bands from each sensor into a single predictor stack. Both raw reflectance bands and pairwise spectral transforms were retained to capture within- and cross-sensor spectral interactions. Second, Linear Discriminant Analysis was applied to both single-sensor and fused datasets to produce comparable low-dimensional feature spaces. Six machine-learning classifiers were then used to benchmark model performance with repeated spatially buffered train–test splits. Results show that Landsat-9 was the strongest single-sensor baseline. Among the fusion strategies, Sentinel-2 + Landsat-9 produced the most consistent improvement and reduced performance variability. Landscape-condition analysis further showed that this fusion was most beneficial in shrubland-dominated and high-terrain fires, where it achieved the highest overall mean accuracy and the fewest failures. In contrast, its benefits were less reliable in evergreen forests, mixed vegetation, and low- to moderate-elevation terrain. In operational settings, the Sentinel-2 + Landsat-9 configuration offers a practical solution for post-fire recovery planning, erosion-risk assessment, watershed management, and ecological monitoring when field observations are available and timely satellite-based information is needed.

Landsat↗
DOE OSTI2024

Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations

Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model’s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A hierarchical stochastic modeling approach for representing point bar geometries and petrophysical property variations

The flow of fluids in point bars is affected by the existence of heterogeneities like shale drapes that are found on the surfaces of inclined heterolithic stratifications. In fact, these shale drapes can act as fluid flow baffles; therefore, developing a framework for modeling point bars and their associated heterogeneities is vital. In this study, a stochastic process-based modeling approach is presented for capturing the main point bar heterogeneities: accretion surfaces (i.e., the aerial heterogeneity) and inclined heterolithic stratifications (i.e., the vertical heterogeneity). The former was modeled using a sine-generation function and the latter, with a sigmoidal function after which they were combined into a 3D point bar model. To ensure proper modeling of petrophysical properties, we developed a more representative gridding scheme which generates curvilinear grids representative of the point bar geometry. This grid was then transformed into a rectilinear grid to allow for geostatistical simulation after which all petrophysical properties were mapped back into the original curvilinear grid. An essential element of this modeling approach is the stochastic representation of shale drapes at the interface between successive accretion surfaces. The workflow was tested using a real field dataset for the Cranfield field, Mississippi. The constructed model was then subjected to a flow simulation study mimicking a CO 2 storage scenario. Various sensitivities were simulated to evaluate the effect of heterogeneities on CO 2 flow within the point-bar. Results demonstrate the importance of representing point-bar related heterogeneity and the spatial distribution of shale drapes on CO 2 plume migration and storage.

58 GEOSCIENCES↗
DOE OSTI2021

Surface Conversion of Single-Crystal Bi 2 Se 3 to β-In 2 Se 3

In this work, we demonstrate that the surface layers of single-crystal layered-2D Bi 2 Se 3 can be converted to layered-2D rhombohedral ß-In 2 Se 3 by annealing under a trimethylindium (TMIn) flux. Samples were prepared in a metalorganic chemical vapor deposition (MOCVD) chamber, then transferred under vacuum to a surface analysis chamber for analysis with low-energy electron diffraction (LEED) and Auger electron spectroscopy. Additional ex situ characterization included x-ray diffraction, transmission-electron microscopy (TEM), energy dispersive x-ray spectroscopy (EDS) elemental mapping, and Raman spectroscopy. The resulting single-crystal ß-In 2 Se 3 adopts the rhombohedral crystal structure (space group R-3m) and orientation of the underlying Bi 2 Se 3 , and the excess Bi atoms generated by this process create an underlying region of Bi-rich Bi x Se y . Due to the difference in bandgap between Bi 2 Se 3 and In 2 Se 3 , this conversion reaction presents a pathway to lateral heterojunctions if only selected regions are converted by masking the surface to spatially define the TMIn exposure. The conversion may also have implications for heteroepitaxy, because the in-plane lattice constants of Bi 2 Se 3 and In 2 Se 3 (0001) surfaces match those of InP and GaAs (111), respectively, and the natural cleavage planes of a layered-2D crystal facilitate substrate removal and reuse.

14 SOLAR ENERGY↗
DOE OSTI2024

Imaging the Meissner effect in hydride superconductors using quantum sensors

By directly altering microscopic interactions, pressure provides a powerful tuning knob for the exploration of condensed phases and geophysical phenomena. Here, the megabar regime represents an interesting frontier, in which recent discoveries include high-temperature superconductors, as well as structural and valence phase transitions. However, at such high pressures, many conventional measurement techniques fail. Here we demonstrate the ability to perform local magnetometry inside a diamond anvil cell with sub-micron spatial resolution at megabar pressures. Our approach uses a shallow layer of nitrogen-vacancy colour centres implanted directly within the anvil; crucially, we choose a crystal cut compatible with the intrinsic symmetries of the nitrogen-vacancy centre to enable functionality at megabar pressures. We apply our technique to characterize a recently discovered hydride superconductor, CeH 9 . By performing simultaneous magnetometry and electrical transport measurements, we observe the dual signatures of superconductivity: diamagnetism characteristic of the Meissner effect and a sharp drop of the resistance to near zero. By locally mapping both the diamagnetic response and flux trapping, we directly image the geometry of superconducting regions, showing marked inhomogeneities at the micron scale. Our work brings quantum sensing to the megabar frontier and enables the closed-loop optimization of superhydride materials synthesis.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Topsoil bulk geochemical compositions - An updated harmonized global dataset

Mineral weathering is a key biogeochemical process because of the capacity of minerals to stabilize organic matter. However, predicting soil weathering status across large spatial areas still isn’t possible due to a lack of global data and theoretical frameworks. To address this knowledge gap, multiple global datasets of bulk topsoil geochemical compositions have been harmonized using R. These datasets document topsoil bulk geochemical compositions across five continents (n = ~16,000 observations). Source data for these observations include the EuroGEOSurveys Geochemical Baseline Database (FOREGS), the US Geological Survey National Geochemical Database (NASGLP), the Geochemical Atlas of Australia (GAA), the US Geological Survey Alaska Geochemical Database (AGD84), the National Cooperative Soil Survey (NCSS), the European Geochemical Mapping of Agricultural Soil (GEMAS), Ecorespira-Amazon (ERA), the New Zealand Geochemical Baseline Survey (NZ_GBS), and the African Soil Information Service (AFSIS). Major elements observed include Aluminum (Al), Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Sodium (Na), Titanium (Ti), Manganese (Mn), Phosphorus (P), Carbon (C), and Sulfur (S). This data package includes the harmonized dataset itself, and the R scripts necessary to harmonize these datasets, in addition to metadata that describes all columns, files, and databases used in this project. Methods & Sampling Step 1 – Databases of geochemical data identified This study aimed to leverage existing measurements of topsoil geochemical data. Databases were first identified and deemed appropriate for inclusion if they were measuring soils and performed these measurements on the <2mm soil fraction. Databases such as NCSS and AGD84 needed more post processing to include in the database and this was done using the NCSS_datamerge_031626 R file and Alaska_USGSmerge_031626 R file, respectively. Step 2 – Database harmonization Once appropriate databases were identified, they were harmonized for ease of analysis using the R script Database_Harmonization_031826. This included removing columns from original datasets that would not be used in analysis (removed columns are noted in the code). Then, data cleaning procedures specific to each dataset were undertaken. This includes standardizing columns to include units and adding metadata columns regarding procedures for analyzing specific elements. Functions for standardizing measurements and units are outline in R files: calculate element_mg_kg_031626, calculate_oxide_wt_perc_031626, change_oxide_caps_031626, and conv_2_numeric_031626. This also included adding a unique identifier for each sample to identify it with its respective database (see CD_ID in data dictionary). Geographic information: Data reflect a compilation of datasets collected globally. Geographic areas covered by each of the datasets include: - EuroGEOSurveys Geochemical Baseline Database (FOREGS) - European continent - North American Soil Geochemical Landscapes (NASGLP) - continental United States and limited parts of Canada (see database key for more details) - National Geochemical Survey of Australia (GAA) - Australia - Alaska geochemical database (AGDB4) - Alaska - National Cooperative Soil Survey (NCSS) - Global measurements, but concentrated in the continental United States - Geochemical data for arable land and land under permanent grass cover in continental Europe (GEMAS) - continental Europe - Ecorespira-Amazon (ERA) - Geochemical data from the Amazon basin - Geochemical baseline data for New Zealand (NZGBS) - New Zealand - Geochemical data collected across continental Africa (AfSIS) - Measurements across Africa

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2024

How Precisely Can Individual Molecules Be Analyzed? A Case Study on Locally Quantifying Forces and Energies Using Scanning Probe Microscopy

Recent advances in scanning probe microscopy methodology have enabled the measurement of tip-sample interactions with picometer accuracy in all three spatial dimensions, thereby providing a detailed site-specific and distance-dependent picture of the related properties. This paper explores the degree of detail and accuracy that can be achieved in locally quantifying probe-molecule interaction forces and energies for adsorbed molecules. Toward this end, cobalt phthalocyanine (CoPc), a promising CO 2 reduction catalyst, was studied on Ag(111) as a model system using low-temperature, ultrahigh vacuum noncontact atomic force microscopy. Data were recorded as a function of distance from the surface, from which detailed three-dimensional maps of the molecule's interaction with the tip for normal and lateral forces as well as the tip-molecule interaction potential were constructed. The data were collected with a CO molecule at the tip apex, which enabled a detailed visualization of the atomic structure. Determination of the tip-substrate interaction as a function of distance allowed isolation of the molecule-tip interactions; when analyzing these in terms of a Lennard-Jones-type potential, the atomically resolved equilibrium interaction energies between the CO tethered to the tip and the CoPc molecule could be recovered. Interaction energies peaked at less than 160 meV, indicating a physisorption interaction. As expected, the interaction was weakest at the aromatic hydrogens around the periphery of the molecule and strongest surrounding the metal center. In conclusion, the interaction, however, did not peak directly above the Co atom but rather in pockets surrounding it.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Development of a 127 Xe calibration source for nEXO

Here, we study a possible calibration technique for the nEXO experiment using a 127 Xe electron capture source. nEXO is a next-generation search for neutrinoless double beta decay (0νββ) that will use a 5-tonne, monolithic liquid xenon time projection chamber (TPC). The xenon, used both as source and detection medium, will be enriched to 90% in 136 Xe. To optimize the event reconstruction and energy resolution, calibrations are needed to map the position- and time-dependent detector response. The 36.3 day half-life of 127 Xe and its small Q-value compared to that of 136 Xe 0νββ would allow a small activity to be maintained continuously in the detector during normal operations without introducing additional backgrounds, thereby enabling in-situ calibration and monitoring of the detector response. In this work we describe a process for producing the source and preliminary experimental tests. We then use simulations to project the precision with which such a source could calibrate spatial corrections to the light and charge response of the nEXO TPC.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Compressive Neural Representations of Volumetric Scalar Fields

Here, we present an approach for compressing volumetric scalar fields using implicit neural representations. Our approach represents a scalar field as a learned function, wherein a neural network maps a point in the domain to an output scalar value. By setting the number of weights of the neural network to be smaller than the input size, we achieve compressed representations of scalar fields, thus framing compression as a type of function approximation. Combined with carefully quantizing network weights, we show that this approach yields highly compact representations that outperform state-of-the-art volume compression approaches. The conceptual simplicity of our approach enables a number of benefits, such as support for time-varying scalar fields, optimizing to preserve spatial gradients, and random-access field evaluation. We study the impact of network design choices on compression performance, highlighting how simple network architectures are effective for a broad range of volumes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Connecting Femtosecond Transient Absorption Microscopy with Spatially Coregistered Time Averaged Optical Imaging Modalities

Multimodal all-optical imaging involving coregistered femtosecond transient absorption microscopy (TAM), time-integrated photoluminescence (PL), and steady-state modalities such as confocal reflectance and transmission offers an appealing approach to gain a comprehensive understanding of complex electronic excited-state phenomena in spatially heterogeneous systems. A unique combination of these modalities allows us to unravel not only the competing electronic excited-state dynamical processes but also the underlying morphological information with simultaneous high temporal and spatial resolution. However, correlating the various images obtained from time-resolved and time-independent modalities is generally nontrivial and particularly challenging when the electronic dynamics under study evolve in both time and space. Here, we demonstrate a new approach for rationally correlating time-resolved microscopy with coregistered time-integrated or steady-state modalities. Specifically, our approach involves an extended global lifetime analysis of the time-resolved microscopic data set to separate distinct dynamical processes taking place on commensurate time scales, and the resulting decay-associated amplitude maps (DAAMs) were applied to explore correlations with the images acquired using time-independent modalities. The feasibility of our approach was validated through analyzing a multimodal data set acquired from a thin film of chloride-containing mixed lead halide perovskites (CH 3 NH 3 PbI 3–x Cl x ) using femtosecond transient absorption, time-integrated PL, and confocal reflectance microscopies. Analysis of the results obtained enable us to gain new insight into the complex ultrafast relaxation dynamics in this highly heterogeneous system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Evaluation of quantitative synchrotron radiation micro-X-ray fluorescence in rice grain

Concentrations of nutrients and contaminants in rice grain affect human health, specifically through the localization and chemical form of elements. Methods to spatially quantify the concentration and speciation of elements are needed to protect human health and characterize elemental homeostasis in plants. Here, an evaluation was carried out using quantitative synchrotron radiation microprobe X-ray fluorescence (SR-µXRF) imaging by comparing average rice grain concentrations of As, Cu, K, Mn, P, S and Zn measured with rice grain concentrations from acid digestion and ICP-MS analysis for 50 grain samples. Better agreement was found between the two methods for high-Z elements. Regression fits between the two methods allowed quantitative concentration maps of the measured elements. These maps revealed that most elements were concentrated in the bran, although S and Zn permeated into the endosperm. Arsenic was highest in the ovular vascular trace (OVT), with concentrations approaching 100 mg kg –1 in the OVT of a grain from a rice plant grown in As-contaminated soil. Quantitative SR-µXRF is a useful approach for comparison across multiple studies but requires careful consideration of sample preparation and beamline characteristics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Reconfigurable unitary transformations of optical beam arrays

Spatial transformations of light are ubiquitous in optics, with examples ranging from simple imaging with a lens to quantum and classical information processing in waveguide meshes. Multi-plane light converter (MPLC) systems have emerged as a platform that promises completely general spatial transformations, i.e., a universal unitary. However, until now, MPLC systems have demonstrated transformations that are far from general, e.g., converting from a Gaussian to Laguerre-Gauss mode. Here, we demonstrate the promise of an MLPC, the ability to impose an arbitrary unitary transformation that can be reconfigured dynamically. Specifically, we consider transformations on superpositions of parallel free-space beams arranged in an array, which is a common information encoding in photonics. We experimentally test the full gamut of unitary transformations for a system of two parallel beams and make a map of their fidelity. We obtain an average transformation fidelity of 0.85 ± 0.03. This high-fidelity suggests that MPLCs are a useful tool for implementing the unitary transformations that comprise quantum and classical information processing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Topological contribution to the Bogoliubov coefficient for cosmological particle production

Particle production in cosmology is often efficiently computed in terms of Bogoliubov transforms. Restricting to a particular class of dispersion relationships, we identify a map between the number of particles produced in a special kinematic limit and a Stokes phenomena related topology of analytic continuation of the Bogoliubov coefficient functions. Intuitively, this kinematic limit corresponds to the long wavelength limit although a more precise description depends on the nature of the curved spacetime. To identify the topology, we reformulate the usual Bogoliubov computations as a type of SU(1, 1) gauged differential equation and utilize a special gauge together with a discrete symmetry that naturally characterizes the dispersion relationship. Using a dark matter model and a nonzero constant spatial curvature model, we estimate how such topological contributions will arise in physical applications. Published by the American Physical Society 2025

Chung, Daniel J. H. (ORCID:0000000343998504)↗
DOE OSTI2024

Mapping charge excitations in generalized Wigner crystals

Transition metal dichalcogenide-based moiré superlattices exhibit strong electron-electron correlations, thus giving rise to strongly correlated quantum phenomena such as generalized Wigner crystal states. Evidence of Wigner crystals in transition metal dichalcogenide moire superlattices has been widely reported from various optical spectroscopy and electrical conductivity measurements, while their microscopic nature has been limited to the basic lattice structure. Theoretical studies predict that unusual quasiparticle excitations across the correlated gap between upper and lower Hubbard bands can arise due to long-range Coulomb interactions in generalized Wigner crystal states. However, the microscopic proof of such quasiparticle excitations is challenging because of the low excitation energy of the Wigner crystal. Here, in this study, we describe a scanning single-electron charging spectroscopy technique with nanometre spatial resolution and single-electron charge resolution that enables us to directly image electron and hole wavefunctions and to determine the thermodynamic gap of generalized Wigner crystal states in twisted WS 2 moiré heterostructures. High-resolution scanning single-electron charging spectroscopy combines scanning tunnelling microscopy with a monolayer graphene sensing layer, thus enabling the generation of individual electron and hole quasiparticles in generalized Wigner crystals. We show that electron and hole quasiparticles have complementary wavefunction distributions and that thermodynamic gaps of ~50 meV exist for the 1/3 and 2/3 generalized Wigner crystal states in twisted WS 2 .

36 MATERIALS SCIENCE↗