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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Deep learning simulations of the microwave sky

Here we present 500 high-resolution, full-sky millimeter-wave deep learning (DL) simulations that include lensed CMB maps and correlated foreground components. We find that these MillimeterDL simulations can reproduce a wide range of non-Gaussian summary statistics matching the input training simulations, while only being optimized to match the power spectra. The procedure we develop in this work enables the capability to mass produce independent full-sky realizations from a single expensive full-sky simulation, when ordinarily the latter would not provide enough training data. We also circumvent a common limitation of high-resolution DL simulations that they be confined to small sky areas, often due to memory or GPU issues; we do this by developing a “stitching” procedure that can recover the large-scale, high-order statistics and avoid discontinuities or repeated features in the maps. In addition, since our network takes as input a full-sky lensing convergence map, it can in principle take a full-sky lensing convergence map from any large-scale structure (LSS) simulation and generate the corresponding lensed CMB and correlated foreground components at millimeter wavelengths; this is especially useful in the current era of combining results from both CMB and LSS surveys, which require a common set of simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Cross-correlation of Dark Energy Survey Year 3 lensing data with ACT and Planck thermal Sunyaev-Zel’dovich effect observations. I. Measurements, systematics tests, and feedback model constraints

Here we present a tomographic measurement of the cross-correlation between thermal Sunyaev-Zel’dovich (TSZ) maps from Planck and the Atacama Cosmology Telescope and weak galaxy lensing shears measured during the first three years of observations of the Dark Energy Survey. This correlation is sensitive to the thermal energy in baryons over a wide redshift range and is therefore a powerful probe of astrophysical feedback. We detect the correlation at a statistical significance of 21σ, the highest significance to date. We examine the TSZ maps for potential contaminants, including cosmic infrared background and radio sources, finding that cosmic infrared background has a substantial impact on our measurements and must be taken into account in our analysis. We use the cross-correlation measurements to test different feedback models. In particular, we model the TSZ using several different pressure profile models calibrated against hydrodynamical simulations. Our analysis marginalizes over redshift uncertainties, shear calibration biases, and intrinsic alignment effects. We also marginalize over Ω m and σ 8 using Planck or DES priors. We find that the data prefer the model with a low amplitude of the pressure profile at small scales, compatible with a scenario with strong active galactic nuclei feedback and ejection of gas from the inner part of the halos. When using a more flexible model for the shear profile, constraints are weaker, and the data cannot discriminate between different baryonic prescriptions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Improved Constraints on Primordial Gravitational Waves using Planck , WMAP, and BICEP/ Keck Observations through the 2018 Observing Season

Here, we present results from an analysis of all data taken by the BICEP2, Keck Array, and BICEP3 CMB polarization experiments up to and including the 2018 observing season. We add additional Keck Array observations at 220 GHz and BICEP3 observations at 95 GHz to the previous 95/150/220 GHz dataset. The Q/U maps now reach depths of 2.8, 2.8, and 8.8 μK CMB arcmin at 95,150, and 220 GHz, respectively, over an effective area of ≈ 600 square degrees at 95 GHz and ≈ 400 square degrees at 150 and 220 GHz. The 220 GHz maps now achieve a signal-to-noise ratio on polarized dust emission exceeding that of Planck at 353 GHz. We take auto- and cross-spectra between these maps and publicly available WMAP and Planck maps at frequencies from 23 to 353 GHz and evaluate the joint likelihood of the spectra versus a multicomponent model of lensed ΛCDM + r + dust + synchrotron + noise . The foreground model has seven parameters, and no longer requires a prior on the frequency spectral index of the dust emission taken from measurements on other regions of the sky. This model is an adequate description of the data at the current noise levels. The likelihood analysis yields the constraint r 0.05 < 0.036 at 95% confidence. Running maximum likelihood search on simulations we obtain unbiased results and find that σ(r) = 0.009 . These are the strongest constraints to date on primordial gravitational waves.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Systematic uncertainties in the measurement of the neutron lifetime using the Lunar Prospector neutron spectrometer

The lifetime of free neutrons measured in the laboratory has a longstanding disparity of ≈9 s. A space-based technique has recently been proposed to independently measure the neutron lifetime using interactions between the galactic cosmic rays and a low atmosphere planetary body. This technique has not produced competitive results yet due to constraints of nonoptimized data that contain large systematic errors. We use data from the neutron spectrometer on-board NASA's Lunar Prospector, and study two large systematics in the measurement of neutron lifetime: the lunar subsurface temperature and the lunar surface composition. We use the HeCd and HeSn neutron spectrometer data when the spacecraft was in a highly elliptical orbit during the orbit insertion period. We report the neutron lifetime using four different models that each have different choices of surface temperature and composition. The 5° [Prettyman et al ., J. Geophys. Res.: Planets 111, 2005JE002656 (2006)] and 2° [Wilson et al., Phys. Rev. C 104, 045501 (2021)] rebinned maps result in 777.6±11.7 s and 739.6±10.8 s, respectively. For the 20° map (Prettyman et al., 2006), constant equatorial and a latitude-dependent temperature model result in 738.6±10.8 s and 767.3±11.2 s, respectively. Increasing the complexities of the models accounting for the systematic effects increase the measured lifetime. However, the reported measurements are not competitive with the laboratory results due to large unaccounted systematics resulting from nonoptimized measurements and modeling assumptions. This work serves as a study of systematic uncertainties for future neutron lifetime measurements using the space-based technique. We estimate the effect on the lifetime from the choice of temperature model to be to be 28.7 ±15.5 s, and choice of compositional map (for 20° and 5° maps) to be 10.3 ±12.2 s.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Alternatives to Contour Visualizations for Power Systems Data

Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system - Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.

MATHEMATICS AND COMPUTING,POWER TRANSMISSION AND D↗
DOE OSTI2024

ICED: An Integrated CGRA Framework Enabling DFVS-Aware Acceleration

oarse-grained reconfigurable arrays (CGRAs) are a promising solution to enable energy-efficient acceleration of applications from different domains. By leveraging reconfiguration at the functional level, they can adapt to significantly different computational patterns. Existing CGRA mapping approaches extract instruction-level parallelism, exploit loop-pipelining opportunities, guarantee the data dependency, and target high throughput of a given loop. However, the recurrence data-dependency in the DFG and the mismatch between required and available computing/communication resources complicate the mapping, and might lead to significant unbalances in the utilization of the CGRA's tiles. This results in wasted power for tiles with low utilization. Applying dynamic voltage and frequency scaling (DVFS) can potentially solve this challenge and improve energy efficiency by adjusting voltage and frequency of different tiles independently. CGRAs have also been successful in accelerating data-dependent streaming applications. However, in these applications, the execution time of each kernel in the pipeline might dynamically vary depending on the characteristics of the input. This also leads to under-utilization of resources for the dynamically changing kernels that do not limit the application throughput. DVFS can also improve energy efficiency for these applications by dynamically changing the voltage and frequency levels of tiles that host non performance-constraining kernels. This paper proposes ICEDTEA -- an integrated DVFS-aware framework to map applications on CGRAs that support power islands. ICEDTEA proposes a CGRA architecture supporting DVFS islands at varying granularity (from a single tile to a group of tiles) and the related DVFS-aware compilation and mapping toolchain. ICEDTEA is the first work that introduces DVFS support for spatio-temporal CGRAs at power-island levels. The experimental evaluation shows that ICEDTEA improves average utilization by 2.3$\times$ and energy-efficiency by 1.32$\times$ over a conventional CGRA. With streaming applications, ICEDTEA improves energy efficiency by 1.12$\times$ over a state-of-the-art CGRA that introduces partial dynamic reconfiguration to adapt to variations in kernels' throughput.

Tan, Cheng↗
DOE OSTI2025

Analog In-Memory Computing for the Synthetic Aperture Radar Polar Format Algorithm

As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute (AIMC) has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. Here, in this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these AIMC systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2-D Fourier transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. In addition, the impact of errors introduced by the analog hardware is explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.

Analog computing↗
DOE OSTI2020

Design and pre-flight performance of SPIDER 280 GHz receivers

In this work we describe upgrades to the Spider balloon-borne telescope in preparation for its second flight, currently planned for December 2021. The Spider instrument is optimized to search for a primordial B-mode polarization signature in the cosmic microwave background at degree angular scales. During its first flight in 2015, Spider mapped ~10% of the sky at 95 and 150 GHz. The payload for the second Antarctic flight will incorporate three new 280 GHz receivers alongside three refurbished 95- and 150 GHz receivers from Spider's first flight. In this work we discuss the design and characterization of these new receivers, which employ over 1500 feedhorn-coupled transition-edge sensors. We describe pre-flight laboratory measurements of detector properties, and the optical performance of completed receivers. These receivers will map a wide area of the sky at 280 GHz, providing new information on polarized Galactic dust emission that will help to separate it from the cosmological signal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Conceptual design of the modular detector and readout system for the CMB-S4 survey experiment

We present the conceptual design of the modular detector and readout system for the Cosmic Microwave Background – Stage four (CMB-S4) ground-based survey experiment. CMB-S4 will map the cosmic microwave background (CMB) and the millimeter-wave sky to unprecedented sensitivity, using 500,000 superconducting detectors observing from Chile and Antarctica to map over 60% of the sky. The fundamental building block of the detector and readout system is a detector module package operated at 100 mK, which is connected to a readout and amplification chain that carries signals out to room temperature. It uses arrays of feedhorn-coupled orthomode transducers (OMT) that collect optical power from the sky onto dc-voltage-biased transition-edge sensor (TES) bolometers. The resulting current signal in the TESs is then amplified by a two-stage cryogenic Superconducting Quantum Interference Device (SQUID) system with a time-division multiplexer to reduce wire count, and matching room-temperature electronics to condition and transmit signals to the data acquisition system. Sensitivity and systematics requirements are being developed for the detector and readout system over wide range of observing bands (20 to 300 GHz) and optical powers to accomplish CMB-S4’s science goals. While the design incorporates the successes of previous generations of CMB instruments, CMB-S4 requires an order of magnitude more detectors than any prior experiment. This requires fabrication of complex superconducting circuits on over 10 m2 of silicon, as well as significant amounts of precision wiring, assembly and cryogenic testing

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

In this work, we present two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport (COT) problems. Both approaches enable conditional sampling and conditional density estimation, which are core tasks in Bayesian inference—particularly in the simulation-based (“likelihood-free”) setting. Our methods represent the target conditional distribution as a transformation of a tractable reference distribution. Obtaining such a transformation, chosen here to be an approximation of the COT map, is computationally challenging even in moderate dimensions. To improve scalability, our numerical algorithms use neural networks to parameterize candidate maps and further exploit the structure of the COT problem. Our static approach approximates the map as the gradient of a partially input convex neural network. It uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives. Our dynamic approach approximates the conditional optimal transport via the flow map of a regularized neural ODE; compared to the static approach, it is slower to train but offers more modeling choices and can lead to faster sampling. We demonstrate both algorithms numerically, comparing them with competing state-of-the-art approaches, using benchmark datasets and simulation-based Bayesian inverse problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fast and Scalable FFT-Based GPU-Accelerated Algorithms for Block-Triangular Toeplitz Matrices with Application to Linear Inverse Problems Governed by Autonomous Dynamical Systems

In this work, we present an efficient and scalable algorithm for performing matrix-vector multiplications (matvecs) for block Toeplitz matrices. Such matrices, which are shift-invariant with respect to their blocks, arise in the context of solving inverse problems governed by autonomous systems, and time-invariant systems in particular. In this article, we consider inverse problems that infer unknown parameters from observational data of a linear time-invariant dynamical system given in the form of partial differential equations (PDEs). Matrix-free Newton-conjugate-gradient methods are often the gold standard for solving these inverse problems, but they require numerous actions of the Hessian on a vector. Matrix-free adjoint-based Hessian matvecs require solution of a pair of linearized forward/adjoint PDE solves per Hessian action, which may be prohibitive for large-scale inverse problems. Time invariance of the forward PDE problem leads to a block Toeplitz structure of the discretized parameter-to-observable (p2o) map defining the mapping from inputs (parameters) to outputs (observables) of the PDEs. This block Toeplitz structure enables us to exploit two key properties: (1) compact storage of the p2o map and its adjoint, and (2) efficient fast Fourier transform–based Hessian matvecs. The proposed algorithm is mapped onto large multi-GPU clusters and achieves more than 80% of peak bandwidth on NVIDIA A100 GPUs. Excellent weak scaling is shown for up to 48 A100 GPUs. For the targeted problems, the implementation executes Hessian matvecs within fractions of a second, which is orders of magnitude faster than can be achieved by conventional matrix-free Hessian matvecs via forward/adjoint PDE solves.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Time-sliced quantum circuit partitioning for modular architectures

Current quantum computer designs will not scale. To scale beyond small prototypes, quantum architectures will likely adopt a modular approach with clusters of tightly connected quantum bits and sparser connections between clusters. We exploit this clustering and the statically-known control flow of quantum programs to create tractable partitioning heuristics which map quantum circuits to modular physical machines one time slice at a time. Specifically, we create optimized mappings for each time slice, accounting for the cost to move data from the previous time slice and using a tunable lookahead scheme to reduce the cost to move to future time slices. We compare our approach to a traditional statically-mapped, owner-computes model. Our results show strict improvement over the static mapping baseline. Here, we reduce the non-local communication overhead by 89.8% in the best case and by 60.9% on average. Our techniques, unlike many exact solver methods, are computationally tractable.

Baker, Jonathan M.↗
DOE OSTI2023

Brief Announcement: Communication Optimal Sparse LU Factorization for Planar Matrices

We introduce a new parallel algorithm for solving sparse LU factorization of planar matrices, which commonly arise in the finite element method for 2D PDEs. Existing scalable methods, such as the multifrontal approach with subtree-to-subcube mapping by Gupta et al. [1] and right-looking with 3D mapping by Sao et al. [2] fail to achieve optimal communication costs for these matrices. Our new algorithm combines 3D mapping and subtree-to-subcube mapping to minimize communication costs while allowing trade-offs between extra memory and reduced communication. We demonstrate that our proposed algorithm attains the communication lower bound up to a factor of O(log log n) in the memory-optimal case and up to a factor of O(log P) in the memory-independent case for an n-dimensional planar sparse matrix on P processors.

Sao, Piyush↗
DOE OSTI2022

Reflection Optical Imaging to Study Oxygen Evolution Reactions

Discovery of high-efficiency water-splitting catalysts is critical for clean hydrogen fuel generation. Quantitative and reliable chemical imaging methods can play a crucial role in providing critical catalyst efficiency information for materials development and screening. Here, we developed a reflection optical imaging method to map the performance of transition metal oxide catalysts for oxygen evolution reaction (OER). The generated high concentration of oxygen in the electrolyte will decrease the refractive index of the solutions near the electrode and change the reflectivity of optical images. We successfully imaged and measured the OER on cobalt oxide, and the localized current density and overpotential distribution maps have been extracted. The oxidation and reduction of cobalt oxide can be imaged with our reflection optical imaging method as well. These maps showed that the catalytic activities are quite different at different locations, and some regions showed much higher activities. This is due to the non-uniformity created by the catalyst synthesis process and demonstrates the importance of using the optical imaging method to map the localized catalytic efficiency. In addition, the NiFe alloy’s OER catalytic activities have been studied as well. The proposed reflection optical imaging method could find many applications in catalyst activity measurement and screening.

Electrochemistry↗
DOE OSTI2020

Java Software Extensibility Library

The Java Software Extensibility Library provides tools that help Java software developers produce flexible and extensible Java applications. The major components include a Plugin Library and a Polymorphic Map data structure. The Plugin Library provides developers with a Plugin Registry and a standard pattern for designing, versioning, registering, retrieving, and annotating plugins. It also enables advanced capabilities such as loading plugins at runtime, and creating/loading encrypted plugin packages. The Polymorphic Map data structure facilitates the storage and retrieval of objects of varying types within a single data structure; while maintaining type safety. The library also provides a standard design pattern for producing application-specific "Domain Maps" and "Closed Domain Maps".SAND2020-3816 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fleschute, Christopher↗
DOE OSTI2025

Imaging Bragg Edge Analysis TooLs for Engineering Structures (iBeatles)

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) provides pulsed neutrons with energies varying from epithermal to cold. In preparation for VENUS, the neutron imaging beamline to be located at beam port 10, we have performed a series of experiments focused on wavelength-dependent radiography and computed tomography for a broad range of applications, from materials science to biological tissues.One of the time-of-flight (TOF) techniques that is of interest to the scientific community is the 2-dimensional mapping of phases and average crystalline plane orientation in samples both ex-situ and during applied stresses such as tensile loading and heating. This technique is known as Bragg edgeimaging and relies on the identification of changes of transmission values, fitting of the edge to measure its displacement, and thus identify the shift in lattice parameter due to stresses. One of the challenges of TOF imaging measurements is the amount of data and the inability to observe Bragg edge shifts in real time during an experiment. Thus, we have been focusing on creating a Python-based interface that allows fast data processing and instantaneous mapping and fitting of the Bragg edges, and their evolution through time. Python libraries and Jupyter notebooks have been implemented to facilitate decision making during an experiment. The advantage of the notebooks is the possibility to guide an experiment as they can quickly process and display Bragg edge data. These notebooks can be used independently, or can be combined in a Python Graphical User Interface (GUI) tool called iBeatles. This interface permits visualization and fitting of the Bragg edges, and ultimately back-projects the fitting results onto the radiographs to display a strain map. Assuming data collection has sufficient statistics, the strain mapping analysis can be performed on a pixel-by-pixel basis. This development is a step forward toward a better user experience at the future VENUS beamline in terms of live feedback and productivity. Analysis that used to take days of switching between different applications can now be done in minutes within the

Bilheux, JeanChristophe [Oak Ridge National Labora↗
DOE OSTI2025

Sulfur Species in Zinc-Rich Condylar Zones of a Rat Temporomandibular Joint

We performed synchrotron-based micro-X-ray absorption near-edge spectroscopy (µ-XANES) coupled with micro-X-ray fluorescence (µ-XRF) for the identification of elements that included biometal zinc (Zn) and nonmetal sulfur (S) (and its species) in the condylar zones of a rat temporomandibular joint (TMJ). Zone-specific spatial localization of biometal Zn and nonmetal S from a materials viewpoint when correlated with hypoxia inducible factor-1α (HIF-1α) (a surrogate for tissue oxygenation) can provide insights into Zn-specific redox pathways at the vulnerable subchondral interface. Histologic localization of Zn, HIF-1α, and sulfur-rich proteoglycans (PGs) were mapped using an optical microscope. The µ-XRF maps coupled with site-specific micro-X-ray diffraction (µ-XRD) patterns were used to underline Zn-incorporated biological apatite in the subchondral bone and the bone of a rat TMJ condyle. Results demonstrated an association between Zn, PG, and HIF-1α histologic maps with µ-XRF, µ-XANES, and µ-XRD data and provided insights into plausible biological S species in Zn-enriched zones of a rat TMJ condyle. Spatially localized Zn and S underscore their roles in cell and tissue functions in a zone-specific manner. Elemental Zn with organic and inorganic S species at the cartilage-bone interface and the biomineral phase of Zn-enriched biological apatite from subchondral bone to condylar bone were ascertained using µ-XRF-XANES and µ-XRF-XRD. The coupled µ-XRF-XANES in situ complemented with µ-XRF-XRD in situ and immunohistochemistry provided valuable biological insights into zone-specific biological pathways in rat TMJ condyles. Based on these data, we present a workflow to reliably map and correlate S species within Zn-enriched regions of cartilage, bone, and their interface. We suggest the use of this correlative and complementary microspectroscopic spatial information for zone-specific localization of biometal Zn and nonmetal S to gain insights into plausible microanatomy-specific oxidative stress in the TMJ.

apatites↗
DOE OSTI2022

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

Engineering↗