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DOE OSTI2016

Initial study of Schroedinger eigenmaps for spectral target detection

Spectral target detection refers to the process of searching for a specific material with a known spectrum over a large area containing materials with different spectral signatures. Traditional target detection methods in hyperspectral imagery (HSI) require assuming the data fit some statistical or geometric models and based on the model, to estimate parameters for defining a hypothesis test, where one class (i.e., target class) is chosen over the other classes (i.e., background class). Nonlinear manifold learning methods such as Laplacian eigenmaps (LE) have extensively shown their potential use in HSI processing, specifically in classification or segmentation. Recently, Schroedinger eigenmaps (SE), which is built upon LE, has been introduced as a semisupervised classification method. In SE, the former Laplacian operator is replaced by the Schroedinger operator. The Schroedinger operator includes by definition, a potential term V that steers the transformation in certain directions improving the separability between classes. In this regard, we propose a methodology for target detection that is not based on the traditional schemes and that does not need the estimation of statistical or geometric parameters. This method is based on SE, where the potential term V is taken into consideration to include the prior knowledge about the target class and use it to steer the transformation in directions where the target location in the new space is known and the separability between target and background is augmented. An initial study of how SE can be used in a target detection scheme for HSI is shown here. Inscene pixel and spectral signature detection approaches are presented. Finally, the HSI data used comprise various target panels for testing simultaneous detection of multiple objects with different complexities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning

Reconstructing 3D granular microstructures within volumes of arbitrary geometries from limited 2D image data is crucial for predicting the material properties, as well as performances of structural components accounting for material microstructural effects. We present a novel generative learning framework that enables exascale reconstruction of granular microstructures within complex 3D geometric volumes. Building upon existing transfer learning techniques using pre-trained convolutional neural networks (CNN), we introduce several key innovations to overcome the difficulties inherent in arbitrary geometries. Our framework incorporates periodic boundary conditions using circular padding techniques, ensuring continuity and representativeness of the reconstructed microstructures. We also introduce a novel seamless transition reconstruction (STR) method that creates statistically equivalent transition zones to integrate multiple pre-existing 3D microstructure volumes. Based on STR, we propose a cost-effective strategy for reconstructing microstructures within complex geometric volumes, minimizing computational waste. Validation through numerical experiments using kinetic Monte Carlo simulations demonstrates accurate reproduction of grain statistics, including grain size distributions and morphology. A case study involving the reconstruction of a 4-blade propeller microstructure illustrates the method’s capability to efficiently handle complex geometries. In conclusion, the proposed framework significantly reduces computational demands while maintaining high reconstruction quality, paving the way for scalable microstructure reconstruction in materials design and analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Dark Energy Survey Year 3 results: Simulation-based 𝑤CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. Here, this work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological 𝑤CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving 2−3× higher figures of merit in the 𝛺 𝑚 − 𝑆 8 plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.

Thomsen, A. [Zurich, ETH] (ORCID:0000000203099021)↗

NASA NTRSDate not supplied

Exploring Lightning and Convective Processes Using the Ground-Radar Multiplatform Precipitation Feature Database

The Multiplatform Precipitation Feature (MPF) database synthesizes coincident spaceborne and ground-based lightning and radar data in a framework of storm-based features, fusing broader spaceborne detection capabilities with process-based, storm-level analysis practices. The MPF database was designed to extend the scale and scope of investigations into the complex connections between precipitation, updrafts, and lightning. The NASA International Space Station Lightning Imaging Sensor (ISS LIS) serves as the source of lightning information for the database. The first iteration of the MPF database leveraged the NASA Global Precipitation Measurement (GPM) mission spaceborne Dual-frequency Precipitation Radar (DPR) to define features, along with contributions of microphysics data and vertical wind retrievals from the GPM Validation Network (VN) of ground-based radar data. The dependency on coincident ISS and GPM satellite overpasses of radars in a dual-Doppler configuration significantly limited the size of the initial database of features, referred to as VNMPFs, but established the database infrastructure and feasibility. We present here a second iteration of the MPF database that omits the GPM DPR and VN, instead incorporating data directly from selected proximal installations of the operational Next Generation Radar (NEXRAD) network that facilitate vertical wind retrievals via dual-Doppler analysis. These features based exclusively on ground-based polarimetric Doppler radar are hereafter referred to as Ground-Radar MPFs (GRMPFs). Removing the restriction of a coincident GPM overpass appreciably increases the size of the GRMPF database while incorporating more detailed information from higher-resolution radar data and retrievals. This expansion allows for unprecedented broad, robust statistical analyses of the electrical, kinematic, and microphysical characteristics of deep convective processes. These results highlight the potential for advancements in lightning meteorology made possible by combining multiple perspectives from global lightning measurements and ground-based radar data.

Lightning↗

DOE OSTI2021

Critical analysis of velocimetry methods for particulate flows from synthetic data

Particle tracking methods that extract high-fidelity particle velocity data from high speed video of particle laden flows is a common experimental technique applied to chemical processes. These measurements are used to better understand the motion of particles and fluids in complex systems and create data against which computational models are validated. However, the methods, codes, and experimental setups all have limitations. It is imperative that practitioners verify the methods and their implementation as well as understand the limitations of experimental setups. This work focuses on quantifying the visible depth of field in a high particle concentration fluidized bed. Following a precedent set by the particle imaging velocimetry community, a particle velocity field is manufactured using a computational fluid dynamics and discrete element method simulation. Photo realistic high-speed videos are rendered based on the simulated data using the three-dimensional creation software Blender. Particle velocities are extracted from the synthetic high-speed videos using three variants of Particle Tracking Velocimetry and Optical Flow Velocimetry methodologies. Here, the tracked results are then compared to the known solution, quantifying the error associated with the assumed visible depth. The results indicate that at depth of one particle diameter, all three particle tracking codes give accurate measurements, largely within 5%. However, the error increases when the full bed video measurements are compared to the known solution at one particle diameter, i.e., mimicking a validation study. Finally, for some statistics the constant depth assumption only increases the error slightly, for others significantly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Lensing without borders – I. A blind comparison of the amplitude of galaxy–galaxy lensing between independent imaging surveys

ABSTRACT Lensing without borders is a cross-survey collaboration created to assess the consistency of galaxy–galaxy lensing signals (ΔΣ) across different data sets and to carry out end-to-end tests of systematic errors. We perform a blind comparison of the amplitude of ΔΣ using lens samples from BOSS and six independent lensing surveys. We find good agreement between empirically estimated and reported systematic errors which agree to better than 2.3σ in four lens bins and three radial ranges. For lenses with zL > 0.43 and considering statistical errors, we detect a 3–4σ correlation between lensing amplitude and survey depth. This correlation could arise from the increasing impact at higher redshift of unrecognized galaxy blends on shear calibration and imperfections in photometric redshift calibration. At zL > 0.54, amplitudes may additionally correlate with foreground stellar density. The amplitude of these trends is within survey-defined systematic error budgets that are designed to include known shear and redshift calibration uncertainty. Using a fully empirical and conservative method, we do not find evidence for large unknown systematics. Systematic errors greater than 15 per cent (25 per cent) ruled out in three lens bins at 68 per cent (95 per cent) confidence at z < 0.54. Differences with respect to predictions based on clustering are observed to be at the 20–30 per cent level. Our results therefore suggest that lensing systematics alone are unlikely to fully explain the ‘lensing is low’ effect at z < 0.54. This analysis demonstrates the power of cross-survey comparisons and provides a promising path for identifying and reducing systematics in future lensing analyses.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2017

Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model

Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5-2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001-2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of greater than 90% can be achieved in mapping winter crops 1.5-2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R(exp. 2) greater than 0.85.

mixture model↗

NASA NTRS2018

Improving the SMAP Level-4 Soil Moisture Product

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level 4 Soil Moisture (L4 SM) product. The L4 SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. The L4 SM data are published with a mean latency of approx. 2.5 days from the time of observation and provide global, three-hourly, 9 km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. In 2018, the product was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high quality space-borne remote sensing observations. Land cover inputs were updated to the GlobCover2009 product, which is based on satellite observations from the Medium Resolution Imaging Spectrometer. Topographic statistics now rely on observations from the Shuttle Radar Topography Mission. Finally, vegetation height inputs are derived from space-borne Lidar measurements. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of soil moisture from the models root-zone excess reservoir into the surface excess reservoir. Specifically, the replenishment of soil moisture near the surface from below under non-equilibrium conditions was substantially reduced, which brings the models surface soil moisture more in line with the SMAP Level 2 and in situ soil moisture. Finally, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4. Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not, and on balance, the overall improvement is modest at best. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation minus forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4 SM product and the lessons learned from the effort.

Reichle, Rolf↗

DOE OSTI2023

A data compression and optimal galaxy weights scheme for Dark Energy Spectroscopic Instrument and weak lensing data sets

Combining different observational probes, such as galaxy clustering and weak lensing, is a promising technique for unveiling the physics of the Universe with upcoming dark energy experiments. The galaxy redshift sample from the Dark Energy Spectroscopic Instrument (DESI) will have a significant overlap with major ongoing imaging surveys specifically designed for weak lensing measurements: the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES), and the Hyper Suprime-Cam (HSC) survey. In this work, we analyse simulated redshift and lensing catalogues to establish a new strategy for combining high-quality cosmological imaging and spectroscopic data, in view of the first-year data assembly analysis of DESI. In a test case fitting for a reduced parameter set, we employ an optimal data compression scheme able to identify those aspects of the data that are most sensitive to cosmological information and amplify them with respect to other aspects of the data. We find this optimal compression approach is able to preserve all the information related to the growth of structures.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2022

TESS in the Extended Mission: A Powerful Tool for Time-Domain Exoplanet Science

Since its launch in 2018, the Transiting Exoplanet Survey Satellite (TESS) has embarked on an ambitious mission to image almost the entire sky in search of nearby transiting exoplanet systems. Upon completion of its Primary Mission in July 2020, TESS transitioned to its first Extended Mission, during which it has returned to both the southern and northern ecliptic hemispheres and revisited most of the targets it observed during the first two years of operation. With future Extended Missions likely, the end-to-end time baseline provided by TESS will significantly exceed that of Kepler, enabling a plethora of scientific investigations into time-domain exoplanet science. TESS has already established itself as an extremely productive tool for studying visible-wavelength exoplanet phase curves. During the Primary Mission, we carried out a systematic phase-curve analysis of more than 30 transiting exoplanet systems. These datasets constrained the dayside hemisphere brightness temperatures, day-night brightness contrasts, and optical geometric albedos of a wide range of gas giant planets, revealing some tantalizing trends that warrant further study. In particular, we uncovered a tentative positive correlation between geometric albedo and equilibrium temperature for gas giants with 1500 < Teq < 3000 K. The new light curves from the Extended Mission have refined the measured astrophysical parameters of the previously studied systems and yielded statistically significant secondary eclipse and phase-curve detections for dozens of more systems. By comparing the phase-curve properties obtained from TESS observations separated by months or years, we have also probed for possible atmospheric variability, as well as transit-timing variations due to orbital decay or additional companions. In this presentation, I will provide an overview of the main results from our ongoing multiyear study of TESS light curves and discuss potentially fruitful avenues for further research as TESS continues its all-sky survey into the future.

Ian Wong↗

DOE OSTI2024

Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching

Diffusion MRI (dMRI) non-invasively maps brain white matter yet necessitates denoising due to low signal-to-noise ratios. Patch2Self (P2S) employing self-supervised techniques and regression on a Casorati matrix effectively denoises dMRI images and has become the new de-facto standard in this field. P2S however is resource intensive both in terms of running time and memory usage as it uses all voxels (n) from all-but-one held-in volumes (d-1) to learn a linear mapping Phi : \mathbb R ^ n x(d-1) \mapsto \mathbb R ^ n for denoising the held-out volume. The increasing size and dimensionality of higher resolution dMRI acquisitions can make P2S infeasible for large-scale analyses. This work exploits the redundancy imposed by P2S to alleviate its performance issues and inspect regions that influence the noise disproportionately. Specifically this study makes a three-fold contribution: (1) We present Patch2Self2 (P2S2) a method that uses matrix sketching to perform self-supervised denoising. By solving a sub-problem on a smaller sub-space so called coreset we show how P2S2 can yield a significant speedup in training time while using less memory. (2) We present a theoretical analysis of P2S2 focusing on determining the optimal sketch size through rank estimation a key step in achieving a balance between denoising accuracy and computational efficiency. (3) We show how the so-called statistical leverage scores can be used to interpret the denoising of dMRI data a process that was traditionally treated as a black-box. Experimental results on both simulated and real data affirm that P2S2 maintains denoising quality while significantly enhancing speed and memory efficiency achieved by training on a reduced data subset.

Fadnavis, Shreyas↗

DOE OSTI2016

TU-H-CAMPUS-JeP2-05: Can Automatic Delineation of Cardiac Substructures On Noncontrast CT Be Used for Cardiac Toxicity Analysis?

Purpose: To evaluate the feasibility of using an automatic segmentation tool to delineate cardiac substructures from computed tomography (CT) images for cardiac toxicity analysis for non-small cell lung cancer (NSCLC) patients after radiotherapy. Methods: A multi-atlas segmentation tool developed in-house was used to delineate eleven cardiac substructures including the whole heart, four heart chambers, and six greater vessels automatically from the averaged 4DCT planning images for 49 NSCLC patients. The automatic segmented contours were edited appropriately by two experienced radiation oncologists. The modified contours were compared with the auto-segmented contours using Dice similarity coefficient (DSC) and mean surface distance (MSD) to evaluate how much modification was needed. In addition, the dose volume histogram (DVH) of the modified contours were compared with that of the auto-segmented contours to evaluate the dosimetric difference between modified and auto-segmented contours. Results: Of the eleven structures, the averaged DSC values ranged from 0.73 ± 0.08 to 0.95 ± 0.04 and the averaged MSD values ranged from 1.3 ± 0.6 mm to 2.9 ± 5.1mm for the 49 patients. Overall, the modification is small. The pulmonary vein (PV) and the inferior vena cava required the most modifications. The V30 (volume receiving 30 Gy or above) for the whole heart and the mean dose to the whole heart and four heart chambers did not show statistically significant difference between modified and auto-segmented contours. The maximum dose to the greater vessels did not show statistically significant difference except for the PV. Conclusion: The automatic segmentation of the cardiac substructures did not require substantial modification. The dosimetric evaluation showed no statistically significant difference between auto-segmented and modified contours except for the PV, which suggests that auto-segmented contours for the cardiac dose response study are feasible in the clinical practice with a minor modification to the PV vessel.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Identification and Analysis of Backwater Nursery Habitats in the Middle Green River, Utah, during 2014, 2017, and 2018 Using High-Resolution Optical Remotely Sensed Imagery

Low-velocity channel-margin habitats, known as backwaters, serve as important nursery habitats for the endangered Colorado pikeminnow (Ptychocheilus lucius) in the middle Green River between Jensen and Ouray, Utah. The backwater synthesis report for the 1987–2013 period (Grippo et al. 2017) reported a decreased number of backwaters per river mile (RM) and an increased mean backwater size across the middle Green River. Information about backwaters is critical for understanding habitat characteristics that are important for Colorado pikeminnow recruitment. The goal of this study was to determine backwater number and size in the middle Green River using high-resolution imagery for 2018 to add to our understanding of long-term trends of backwater habitat availability across the reach. For comparative analysis of backwater habitats between 2004 and 2018, information for 2004, 2006, 2013, 2014 and 2017 from our previous studies was also utilized (Grippo et al. 2017; Hamada et al. 2017, 2021, and in review). Across the 2004, 2006, 2013, 2014, 2017, and 2018 study years, mean daily flow at the Jensen gage during image collection ranged from 1,220 cfs (2004) to 2,940 cfs (2014), which was approximately 140% greater in 2014 than in 2004. The number of delineated backwaters ranged from 88 (2013) to 245 (2017), and total backwater area ranged from 118,938 m 2 (2006) to 209,611 m 2 (2014). Mean backwater size ranged from 822 m 2 (2017) to 1,508 m 2 (2018), but there was considerable variability in backwater size for each of the study years. Backwater areas differed significantly among the study years based on a Kruskal-Wallis Rank Sum test ( P < 0.001). Pairwise Wilcoxon tests indicated that the median backwater areas in 2017 and 2014 were significantly lower than in the other sample years ( P ≤ 0.05), even though these years had the highest total backwater area. 2018 had the greatest median backwater area, although the distribution of backwater area values was not statistically different from backwater areas measured in 2013 ( P > 0.05). These two years also had the lowest maximum backwater area values among all sample years. The distribution of backwater area values was not significantly different ( P > 0.05) among the 2006, 2004, and 2013 sample years.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2011

Analysis of Co-Located MODIS and CALIPSO Observations Near Clouds

The purpose of this paper is to help researchers combine data from different satellites and thus gain new insights into two critical yet poorly understood aspects of anthropogenic climate change, aerosol-cloud interactions and aerosol radiative effects, For this, the paper explores whether cloud information from the Aqua satellite's MODIS instrument can help characterize systematic aerosol changes near clouds by refining earlier perceptions of these changes that were based on the CALIPSO satellite's CALIOP instrument. Similar to a radar but using visible and ncar-infrared light, CALIOP sends out laser pulses and provides aerosol and cloud information along a single line that tracks the satellite orbit by measuring the reflection of its pulses. In contrast, MODIS takes images of reflected sunlight and emitted infrared radiation at several wavelengths, and covers wide areas around the satellite track. This paper analyzes a year-long global dataset covering all ice-free oceans, and finds that MODIS can greatly help the interpretation of CALIOP observations, especially by detecting clouds that lie outside the line observed by CALlPSO. The paper also finds that complications such as differences in view direction or clouds drifting in the 72 seconds that elapse between MODIS and CALIOP observations have only a minor impact. The study also finds that MODIS data helps refine but does not qualitatively alter perceptions of the systematic aerosol changes that were detected in earlier studies using only CALIOP data. It then proposes a statistical approach to account for clouds lying outside the CALIOP track even when MODIS cannot as reliably detect low clouds, for example at night or over ice. Finally, the paper finds that, because of variations in cloud amount and type, the typical distance to clouds in maritime clear areas varies with season and location. The overall median distance to clouds in maritime clear areas around 4-5 km. The fact that half of all clear areas is closer than 5 km to clouds implies that pronounced near-cloud changes in aerosol properties have significant implications for overall clear-sky characteristics, including the radiative impact of aerosols.

Varnai, Tamas↗

DOE OSTI2016

Planck intermediate results: XXXVIII. E - and B -modes of dust polarization from the magnetized filamentary structure of the interstellar medium

The quest for a B-mode imprint from primordial gravity waves on the polarization of the cosmic microwave background (CMB) requires the characterization of foreground polarization from Galactic dust. In this paper, we present a statistical study of the filamentary structure of the 353 GHz Planck Stokes maps at high Galactic latitude, relevant to the study of dust emission as a polarized foreground to the CMB. We filter the intensity and polarization maps to isolate filaments in the range of angular scales where the power asymmetry between E-modes and B-modes is observed. Using the Smoothed Hessian Major Axis Filament Finder (SMAFF), we identify 259 filaments at high Galactic latitude, with lengths larger or equal to 2° (corresponding to 3.5 pc in length for a typical distance of 100 pc). Thesefilaments show a preferred orientation parallel to the magnetic field projected onto the plane of the sky, derived from their polarization angles. We present mean maps of the filaments in Stokes I, Q, U, E, and B, computed by stacking individual images rotated to align the orientations of the filaments. Combining the stacked images and the histogram of relative orientations, we estimate the mean polarization fraction of the filaments to be 11%. Furthermore, we show that the correlation between the filaments and the magnetic field orientations may account for the E and B asymmetry and the C ℓ TE /C ℓ EE ratio, reported in the power spectra analysis of the Planck353 GHz polarization maps. Finally, future models of the dust foreground for CMB polarization studies will need to take into account the observed correlation between the dust polarization and the structure of interstellar matter.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations

The association between spectral reflectance and canopy processes remains challenging for quantifying large-scale canopy phenological cycles in tropical forests. In this paper, we used a tower-mounted hyperspectral camera in an eastern Amazon forest to assess how canopy spectral signals of three species are linked with phenological processes in the 2012 dry season. We explored different approaches to disentangle the spectral components of canopy phenology processes and analyze their variations over time using 17 images acquired by the camera. The methods included linear spectral mixture analysis (SMA); principal component analysis (PCA); continuum removal (CR); and first-order derivative analysis. In addition, three vegetation indices potentially sensitive to leaf flushing, leaf loss and leaf area index (LAI) were calculated: the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and the entitled Green-Red Normalized Difference (GRND) index. We inspected also the consistency of the camera observations using Moderate Resolution Imaging Spectroradiometer (MODIS) and available phenological data on new leaf production and LAI of young, mature and old leaves simulated by a leaf demography-ontogeny model. The results showed a diversity of phenological responses during the 2012 dry season with related changes in canopy structure and greenness values. Because of the differences in timing and intensity of leaf flushing and leaf shedding, Erisma uncinatum, Manilkara huberi and Chamaecrista xinguensis presented different green vegetation (GV) and non-photosynthetic vegetation (NPV) SMA fractions; distinct PCA scores; changes in depth, width and area of the 681-nm chlorophyll absorption band; and variations over time in the EVI, GRND and NDVI. At the end of dry season, GV increased for Erisma uncinatum, while NPV increased for Chamaecrista xinguensis. For Manilkara huberi, the NPV first increased in the beginning of August and then decreased toward September with new foliage. Variations in red-edge position were not statistically significant between the species and across dates at the 95% confidence level. The camera data were affected by view-illumination effects, which reduced the SMA shade fraction over time. When MODIS data were corrected for these effects using the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), we observed an EVI increase toward September that closely tracked the modeled LAI of mature leaves (3–5 months). Compared to the EVI, the GRND was a better indicator of leaf flushing because the modeled production of new leaves peaked in August and then declined in September following the GRND closely. Finally, while the EVI was more related to changes in mature leaf area, the GRND was more associated with new leaf flushing.

54 ENVIRONMENTAL SCIENCES↗