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

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Visualizing nanoscale 3D compositional fluctuation of lithium in advanced lithium-ion battery cathodes

The distribution and concentration of lithium in Li-ion battery cathodes at different stages of cycling is a pivotal factor in determining battery performance. Non-uniform distribution of the transition metal cations has been shown to affect cathode performance; however, the Li is notoriously challenging to characterize with typical high-spatial-resolution imaging techniques. Here, for the first time, laser–assisted atom probe tomography is applied to two advanced Li-ion battery oxide cathode materials—layered Li 1.2 Ni 0.2 Mn 0.6 O 2 and spinel LiNi 0.5 Mn 1.5 O 4 —to unambiguously map the three dimensional (3D) distribution of Li at sub-nanometer spatial resolution and correlate it with the distribution of the transition metal cations (M) and the oxygen. The as-fabricated layered Li 1.2 Ni 0.2 Mn 0.6 O 2 is shown to have Li-rich Li 2 MO 3 phase regions and Li-depleted Li(Ni 0.5 Mn 0.5 )O 2 regions while in the cycled layered Li 1.2 Ni 0.2 Mn 0.6 O 2 an overall loss of Li and presence of Ni rich regions, Mn rich regions and Li rich regions are shown in addition to providing the first direct evidence for Li loss on cycling of layered LNMO cathodes. The spinel LiNi 0.5 Mn 1.5 O 4 cathode is shown to have a uniform distribution of all cations. These results were additionally validated by correlating with energy dispersive spectroscopy mapping of these nanoparticles in a scanning transmission electron microscope. Thus, we have opened the door for probing the nanoscale compositional fluctuations in crucial Li-ion battery cathode materials at an unprecedented spatial resolution of sub-nanometer scale in 3D which can provide critical information for understanding capacity decay mechanisms in these advanced cathode materials.

36 MATERIALS SCIENCE↗
DOE OSTI2015

A fast method for optical simulation of flood maps of light-sharing detector modules

Optical simulation of the detector module level is highly desired for Position Emission Tomography (PET) system design. Commonly used simulation toolkits such as GATE are not efficient in the optical simulation of detector modules with complicated light-sharing configurations, where a vast amount of photons need to be tracked. Here, we present a fast approach based on a simplified specular reflectance model and a structured light-tracking algorithm to speed up the photon tracking in detector modules constructed with polished finish and specular reflector materials. We also simulated conventional block detector designs with different slotted light guide patterns using the new approach and compared the outcomes with those from GATE simulations. And while the two approaches generated comparable flood maps, the new approach was more than 200–600 times faster. The new approach has also been validated by constructing a prototype detector and comparing the simulated flood map with the experimental flood map. The experimental flood map has nearly uniformly distributed spots similar to those in the simulated flood map. In conclusion, the new approach provides a fast and reliable simulation tool for assisting in the development of light-sharing-based detector modules with a polished surface finish and using specular reflector materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

EXTINCTION DROPLET SIZING MEASUREMENTS IN DIESEL RELEVANT SPRAYS

A new diagnostic for the quantification of Sauter Mean Diameter (SMD) in high-pressure fuel sprays has been recently developed using combined optical and x-ray measurements at the Georgia Institute of Technology and Argonne National Laboratory, respectively. This diagnostic utilizes liquid scattering extinction measurements from diffuse back-illumination (DBI) imaging, conducted at Georgia Tech, and liquid absorption measurements from x-ray radiography, conducted at Argonne’s Advanced Photon Source. The new diagnostic, entitled the Scattering Absorption Measurement Ratio (SAMR), quantifies two-dimensional distributions of path-integrated SMD, enabling construction of the spatial history of drop size development within practical fuel sprays. This technique offers unique benefits over conventional drop-sizing methods in that it can be more robust in optically dense regions of the spray, while also providing high spatial resolution of the corresponding droplet field. The spatially-resolved SMD measurements that result from the SAMR diagnostic will be especially valuable to the engine modeling community for the quantitative validation of spray submodels in engine CFD codes. The methodology for quantification of SMD distributions using the SAMR technique has been previously introduced. This thesis aims to extend the initial developmen of SAMR technique by presenting in detail: the experimental methodologies used in the SAMR technique, including the development of an ideal DBI setup for this technique; the data processing methodologies developed by the author; two-dimensional Sauter Mean Diameter measurements within diesel sprays for various experimental conditions; a summary of the various sources of measurement uncertainty; an assessment for how the sources of uncertainty affect the quantified SMD; and paths to improve the technique moving forward. The SMD results show that for low ambient density conditions droplets decrease in size as radial and axial position increases. For high ambient density conditions, however, the droplets show a stable size near the spray centerline and steadily increase in size as distance from the centerline increases. Additionally, this work provides 2-D droplet sizing maps which are particularly useful for model validation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

EXTINCTION DROPLET SIZING MEASUREMENTS IN DIESEL RELEVANT SPRAYS

A new diagnostic for the quantification of Sauter Mean Diameter (SMD) in high-pressure fuel sprays has been recently developed using combined optical and x-ray measurements at the Georgia Institute of Technology and Argonne National Laboratory, respectively. This diagnostic utilizes liquid scattering extinction measurements from diffuse back-illumination (DBI) imaging, conducted at Georgia Tech, and liquid absorption measurements from x-ray radiography, conducted at Argonne’s Advanced Photon Source. The new diagnostic, entitled the Scattering Absorption Measurement Ratio (SAMR), quantifies two-dimensional distributions of path-integrated SMD, enabling construction of the spatial history of drop size development within practical fuel sprays. This technique offers unique benefits over conventional drop-sizing methods in that it can be more robust in optically dense regions of the spray, while also providing high spatial resolution of the corresponding droplet field. The spatially-resolved SMD measurements that result from the SAMR diagnostic will be especially valuable to the engine modeling community for the quantitative validation of spray submodels in engine CFD codes. The methodology for quantification of SMD distributions using the SAMR technique has been previously introduced. This thesis aims to extend the initial development of the SAMR technique by presenting in detail: the experimental methodologies used in the SAMR technique, including the development of an ideal DBI setup for this technique; the data processing methodologies developed by the author; two-dimensional Sauter Mean Diameter measurements within diesel sprays for various experimental conditions; a summary of the various sources of measurement uncertainty; an assessment for how the sources of uncertainty affect the quantified SMD; and paths to improve the technique moving forward. The SMD results show that for low ambient density conditions droplets decrease in size as radial and axial position increases. For high ambient density conditions, however, the droplets show a stable size near the spray centerline and steadily increase in size as distance from the centerline increases. Additionally, this work provides 2-D droplet sizing maps which are particularly useful for model validation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Application of PCM Validation to BFBT Experiments

In support of model validation, this summary applies a recently proposed methodology, denoted hereinafter by physics-guided coverage mapping (PCM), to the estimation of void fraction in heated channels using the OECD/NEA full size fine mesh bundle test (BFBT) data. PCM here is developed as a quantitative approach that allows for a rigorous scaling of experimental biases to the application domain, i.e., the range of the conditions intended for model application. PCM aims to address some of the conceptual and computational difficulties associated with model validation exercises where it is essential to establish with a certain confidence that the available experiments provide enough evidence to support the use of the model for its intended range of application. To our knowledge, this methodology represents a first-of-a-kind application of physics models to support validation, in a manner that precludes parameters calibration, and avoids the introduction of expert-based scale-distortion uncertainties. (authors)

42 ENGINEERING↗
DOE OSTI2015

Strain mapping at nanometer resolution using advanced nano-beam electron diffraction

In this work, we report on the development of a nanometer scale strain mapping technique by means of scanning nano-beam electron diffraction. Only recently possible due to fast acquisition with a direct electron detector, this technique allows for strain mapping with a high precision of 0.1% at a lateral resolution of 1 nm for a large field of view reaching up to 1 μm. We demonstrate its application to a technologically relevant strain-engineered GaAs/GaAsP hetero-structure and show that the method can even be applied to highly defected regions with substantial changes in local crystal orientation. Strain maps derived from atomically resolved scanning transmission electron microscopy images were used to validate the accuracy, precision and resolution of this versatile technique.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Anticipating global terrestrial ecosystem state change using FLUXNET

Ecosystems can be characterized as complex systems that traverse a variety of functional and structural states in response to changing bioclimatic forcings. A central challenge of global change biology is the robust empirical description of these states and state transitions. An ecosystem's functional state can be empirically described using Process Networks (PN) that use timeseries observations to determine the strength of process-level functional couplings between ecosystem components. A globally extensive source of in-situ observations of terrestrial ecosystem dynamics is the FLUXNET eddy-covariance network that provides standardized observations of micrometeorology and carbon, water, and energy flux dynamics. We employ the LaThuile FLUXNET synthesis dataset to delineate each month's functional state for 204 sites, yielding the LaThuile PN version 1.0 database that describes the strength of an ecosystem's functional couplings from air temperature and precipitation to carbon fluxes during each site-month. Then we calculate the elasticity of these couplings to seasonal scale forcings: air temperature, precipitation, solar radiation, and phenophase. Finally, we train artificial neural networks to extrapolate these elasticities from 204 sites to the globe, yielding maps of the estimated functional elasticity of every terrestrial ecosystem's functional states to changing seasonal bioclimatic forcings. These maps provide theoretically novel resource that can be used to anticipate ecological state transitions in response to climate change and to validate process-based models of ecological change. These elasticity maps show that each ecosystem can be expected to respond uniquely to changing forcings. Tropical forests, hot deserts, savannas, and high elevations are most elastic to climate change, and elasticity of ecosystems to seasonal air temperature is on average an order of magnitude higher than elasticity to other bioclimatic forcings. We also observe a reasonable amount of moderate relationships between functional elasticity and structural state change across different ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

2D Dose Reconstruction by Artificial Neural Network for Pretreatment Verification of IMRT Fields

The use of intensity-modulated radiation therapy (IMRT) is developing rapidly in clinical routines. Because of the high complexity and uniqueness of IMRT treatment plans, patient-specific pretreatment quality assurance is generally considered a necessary prerequisite for patient treatment. In this work, we proposed a modified methodology of electronic portal imaging device (EPID)–based dose validation for pretreatment verification of IMRT fields by applying artificial neural networks (ANNs). The ANN must be trained and validated before use for pretreatment dose verification. For this purpose, 20 EPID fluence maps of IMRT prostate anterior-posterior fields were used as an input for ANN (feed forward type) and a dose map of those fluence maps that were predicted by treatment planning system as an output for ANN. After the training and validation of the neural network, the analysis of 10 IMRT prostate anterior-posterior fields showed excellent agreement between ANN output and dose map predicted by the treatment planning system. The average overall fields pass rate was 96.0% ± 0.1% with 3 mm/3% criteria. The results indicated that the ANN can be used as a low-cost, fast, and powerful tool for pretreatment dose verification, based on an EPID fluence map.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

The picasso map-making code: application to a simulation of the QUIJOTE northern sky survey

ABSTRACT Map-making is an important step for the data analysis of cosmic microwave background (CMB) experiments. It consists of converting the data, which are typically a long, complex, and noisy collection of measurements, into a map, which is an image of the observed sky. We present in this paper a new map-making code named picasso (Polarization and Intensity CArtographer for Scanned Sky Observations), which was implemented to construct intensity and polarization maps from the Multi Frequency Instrument (MFI) of the QUIJOTE (Q-U-I Joint TEnerife) CMB polarization experiment. picasso is based on the destriping algorithm, and is suited to address specific issues of ground-based microwave observations, with a technique that allows the fit of a template function in the time domain, during the map-making step. This paper describes the picasso code, validating it with simulations and assessing its performance. For this purpose, we produced realistic simulations of the QUIJOTE-MFI survey of the northern sky (approximately ∼20 000 deg2), and analysed the reconstructed maps with picasso, using real and harmonic space statistics. We show that, for this sky area, picasso is able to reconstruct, with high fidelity, the injected signal, recovering all the scales with ℓ > 10 in TT, EE, and BB. The signal error is better than 0.001 per cent at 20 < ℓ < 200. Finally, we validated some of the methods that will be applied to the real wide-survey data, like the detection of the CMB anisotropies via cross-correlation analyses. Despite that the implementation of picasso is specific for QUIJOTE-MFI data, it could be adapted to other experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Self-Navigating Robotic Assistant for Long-term Wide Area Floor Contamination Monitoring - 18213

For Routine Contamination Testing (RCT) of an operational nuclear facility or one undergoing Decontamination and Decommissioning (D and D), workers utilize specified travel corridors (enclosed hallways or areas designated with tape or rope in open spaces) to minimize the risk of spreading contamination. Radiation health technicians must continually monitor these areas for contamination to minimize worker dose and prevent its spread to uncontrolled areas. For many sites, it is impractical for the technician to adequately survey such areas. The Nuclear Robotics Group (NRG) at UT Austin has developed a self-navigating robotic platform equipped with radiation detectors to augment the technician to improve both survey coverage and frequency. While maintaining a controlled velocity and floor-to-sensor distance, the system takes radiation measurements to identify and/or localize contamination sources. Readings above background levels are reported to a remote workstation to allow technicians to then respond accordingly. The completion of these mundane tasks by a robot allows the trained technicians to focus instead on operational tasks, D and D, or responding to alarms. The robot generates a map using a Lidar range sensor with established Simultaneous Localization and Mapping (SLAM) techniques. Range data is then compared to previously generated maps for robot localization and autonomous navigation. To maximize coverage, the robot executes a Complete Coverage Path Planning (CCPP) algorithm using a static map that is discretized into cells to assure comprehensive yet efficient coverage. As the system executes the generated plan, it builds a radiation map of the area for the technician to validate and verify the results. If contamination is detected, the robot will minimize the spread of contamination by stopping, sounding an alarm, and alerting the remote technician. The proposed system allows for long-term deployment with minimal human intervention. It is robust to both highly dynamic obstacles (i.e. personnel) and low dynamic changes (i.e. temporally stored cart) in the environment. This is accomplished through continuous spatiotemporal updating of the static map, and the utilization of a supervisory controller that allows the system to reliably respond to unexpected events or states (i.e. low battery, closed door, etc.). While the supervisory controller can respond to temporary complications, the system also recognizes the environment can change slowly over time necessitating that the CCPP automatically be updated to account for observed 'permanent' changes to the environment. To do this, the robot uses intermediate probabilistic data to make intelligent planning decisions offline while still maintaining local reactive obstacle avoidance and safety features. The self-updating of the map allows the robot to run longer without the need to remake the map, leading to more consistent monitoring data and less maintenance from the human technician. This paper will review the current system hardware, the CCPP algorithm, the supervisory controller, the probabilistic algorithm for identifying permanent changes, and recent experiments at UT Austin to experimentally validate the reported sensor coverage. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2011

A DEEP 1.2 mm MAP OF THE LOCKMAN HOLE NORTH FIELD

We present deep 1.2 mm continuum mapping of a 566 arcmin{sup 2} area within the Lockman Hole North (LHN) field, previously a target of the Spitzer Wide-Area Infrared Extragalactic survey and extremely deep 20 cm mapping with the Very Large Array, which we have obtained using the Max-Planck millimeter bolometer (MAMBO) array on the IRAM 30 m telescope. After filtering, our full map has an rms sensitivity ranging from 0.45 to 1.5 mJy beam{sup -1}, with an average of 0.75 mJy beam{sup -1}. Using the pixel flux distribution (PFD) in a map made from our best data, we determine the shape, normalization, and approximate flux density cutoff for 1.2 mm number counts well below our nominal sensitivity and confusion limits. After validating our full data set through comparison with this map, we successfully detect 41 1.2 mm sources with signal-to-noise ratio (S/N) > 4.0 and S{sub 1.2{sub mm}} {approx_equal} 2-5 mJy. We use the most significant of these detections to directly determine the integral number counts down to 1.8 mJy, which are consistent with the results of the PFD analysis. Ninety-three percent of our 41 individual detections have 20 cm counterparts, 49% have Spitzer/MIPS 24 {mu}m counterparts, and one may have a significant Chandra X-ray counterpart. We resolve {approx_equal} 3% of the cosmic infrared background (CIB) at 1.2 mm into significant detections and directly estimate a 0.05 mJy faint-end cutoff for the counts that is consistent with the full intensity of the 1.2 mm CIB. The median redshift of our 17 detections with spectroscopic or robust photometric redshifts is z{sub median} = 2.3, and rises to z{sub median} = 2.9 when we include redshifts estimated from the radio/far-infrared spectral index. By using a nearest neighbor and angular correlation function analysis, we find evidence that our S/N > 4.0 detections are clustered at the 95% confidence level.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

58 GEOSCIENCES↗
DOE OSTI2024

High-throughput reaction discovery for Cs–Pb–Br nanocrystal synthesis

High-throughput reaction discovery is necessary to understand complex reaction spaces for inorganic nanocrystal synthesis. Here, we implemented a high-throughput continuous flow millifluidic reactor to perform reaction discovery for Cs–Pb–Br nanocrystal synthesis using a ligand assisted reprecipitation (LARP)-type approach. 3D-printed flow resistors enable the screening of up to 16 different mixing ratios within a single 90 s run, allowing for >270 different precursor concentration ratios to be quickly tested to explore the phase space that results in CsPbBr 3 , Cs 4 PbBr 6 , a biphasic mixture, or no product. To construct a full phase map from these high-throughput experiments, a neural network was trained and validated to predict the product composition (~500 000 points in precursor concentration space). The phase map predicts product composition/phase as a function of Cs–Pb–Br feed ratio. As a result, this approach demonstrates how high-throughput flow chemistry can be used in tandem with machine learning to rapidly explore nanocrystal reaction spaces in flow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps

Cryoelectron microscopy requires molecular modeling for refinement of structures. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins. Here, we introduce CryoFold, a pipeline of molecular dynamics simulations that determines ensembles of protein structures by integrating density data of varying sparsity at 3–5 Å resolution with sequence information and coarse-grained topological knowledge of the protein folds. We present six examples, folding proteins between 72 and 2,000 residues, including large membrane and multi-domain systems, and results from two Electron Microscopy Data Bank (EMDB) competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python graphical user interface (GUI).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Single-shot in-line x-ray phase-contrast imaging of void-shockwave interactions in fusion energy materials

Recent breakthroughs in nuclear fusion, specifically the report of reactions exceeding scientific breakeven at the National Ignition Facility (NIF), highlight the potential of inertial fusion energy (IFE) as a sustainable and virtually limitless energy source. However, further progress in IFE requires characterization of defects in ablator materials and how they affect fuel capsule compression. Voids within the ablator can degrade energy yield, but their impact on the density distribution has primarily been studied through simulations, with limited high-resolution experimental validation. To address this, we used the x-ray free-electron laser (XFEL) at the matter in extreme conditions (MECs) instrument at the Linac coherent light source (LCLS) to capture 2D x-ray phase-contrast (XPC) images of a void-bearing sample with a composition similar to inertial confinement fusion (ICF) ablators. By driving a compressive shockwave through the sample using MEC's long-pulse laser system, we analyzed how voids influence shockwave propagation and density distribution during compression. To quantify this impact, we extracted phase information using two phase retrieval algorithms. First, we applied the contrast transfer function (CTF) method, paired with Tikhonov regularization and a fast optimization approach to generate an initial phase estimate. We then refined the result using a projected gradient descent (PGD) method that works directly with the sample's refractive index. Comparing these results with radiation adaptive grid Eulerian (xRAGE) radiation hydrodynamic simulations enables identification of model validation needs or improvements. By calculating phase maps in situ, it becomes possible to reconstruct areal density maps, improving understanding of laser-capsule interactions and advancing IFE research.

Hodge, D. S. [Colorado State Univ., Fort Collins, ↗
DOE OSTI2026

SPT-3G D1: CMB temperature and polarization power spectra and cosmology from 2019 and 2020 observations of the SPT-3G main field

Here, we present measurements of the temperature and E-mode polarization angular power spectra of the cosmic microwave background (CMB) from observations of 4% of the sky with SPT-3G, the current camera on the South Pole Telescope (SPT). The maps used in this analysis are the deepest used in a CMB TT / TE / EE analysis to date. The maps and resulting power spectra have been validated through blind and unblind tests. The measurements of the lensed EE and TE spectra are the most precise to date at ℓ = 1800 – 4000 and ℓ = 2200 – 4000 , respectively. Combining our TT / TE / EE spectra with previously published SPT-3G CMB lensing results, we find parameters for the standard Λ cold dark matter ( Λ CDM ) model consistent with Planck and ACT DR6 with comparable constraining power. We report a Hubble constant of H 0 = 66.66 ± 0.60 km s - 1 Mpc - 1 from SPT-3G alone, 6.2 σ away from local measurements from SH0ES. For the first time, combined ground-based ( SPT + ACT ) CMB primary and lensing data have reached Planck’s constraining power on some parameters, a milestone for CMB cosmology. The combination of these three CMB experiments yields the tightest CMB constraints to date, with H 0 = 67.19 ± 0.38 km s - 1 Mpc - 1 , and the amplitude of clustering σ 8 = 0.8137 ± 0.0037 . CMB data alone show no evidence for physics beyond Λ CDM , however, we observe a 2.8 σ difference in Λ CDM between CMB and baryon acoustic oscillation (BAO) results from DESI-DR2, which is relaxed in extended models. The combination of CMB and BAO yields 2 − 3 σ shifts from Λ CDM in the curvature of the Universe, the amplitude of CMB lensing, or the dark energy equation of state. It also drives mild preferences for models that address the Hubble tension through modified recombination or variations in the electron mass in a nonflat Universe. This work highlights the growing power of ground-based CMB experiments and lays a foundation for further cosmological analyses with SPT-3G.

Camphuis, Etienne [Sorbonne Université; CNRS; Inst↗
DOE OSTI2023

Full Counting Statistics of Charge in Chaotic Many-Body Quantum Systems

We investigate the full counting statistics of charge transport in U(1)-symmetric random unitary circuits. We consider an initial mixed state prepared with a chemical potential imbalance between the left and right halves of the system and study the fluctuations of the charge transferred across the central bond in typical circuits. Using an effective replica statistical mechanics model and a mapping onto an emergent classical stochastic process valid at large on-site Hilbert space dimension, we show that charge transfer fluctuations approach those of the symmetric exclusion process at long times, with subleading t –1/2 quantum corrections. Here, we discuss our results in the context of fluctuating hydrodynamics and macroscopic fluctuation theory of classical nonequilibrium systems and check our predictions against direct matrix-product state calculations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Mini-apps for high performance data analysis

Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating `Big Data' from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the `Big Data' hardware and software architecture landscape is constantly evolving, (ii) newer architectures impose new programming models, and (iii) data-parallel kernels of analysis algorithms and their performance facets on different architectures are poorly understood. To address these problems, we have: (i) identified scalable data-parallel kernels of popular data analysis algorithms, (ii) implemented `Mini-Apps' of those kernels using different programming models (e.g. Map Reduce, MPI, etc.), (iii) benchmarked and validated the performance of the kernels in diverse architectures. In this paper, we discuss two of those Mini-Apps and show the execution of principal component analysis built as a workflow of the Mini-Apps. We show that Mini-Apps enable scientists to (i) write domain-specific data analysis code that scales on most HPC hardware and (ii) and offers the ability (most times with over a 10x speed-up) to analyze data sizes 100 times the size of what off-the-shelf desktop/workstations of today can handle.

Sukumar, Sreenivas R.↗