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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Unfolded states under folding conditions accommodate sequence-specific conformational preferences with random coil-like dimensions

Proteins are marginally stable molecules that fluctuate between folded and unfolded states. Here, we provide a high-resolution description of unfolded states under refolding conditions for the N-terminal domain of the L9 protein (NTL9). We use a combination of time-resolved Förster resonance energy transfer (FRET) based on multiple pairs of minimally perturbing labels, time-resolved small-angle X-ray scattering (SAXS), all-atom simulations, and polymer theory. Upon dilution from high denaturant, the unfolded state undergoes rapid contraction. Although this contraction occurs before the folding transition, the unfolded state remains considerably more expanded than the folded state and accommodates a range of local and nonlocal contacts, including secondary structures and native and nonnative interactions. Paradoxically, despite discernible sequence-specific conformational preferences, the ensemble-averaged properties of unfolded states are consistent with those of canonical random coils, namely polymers in indifferent (theta) solvents. These findings are concordant with theoretical predictions based on coarse-grained models and inferences drawn from single-molecule experiments regarding the sequence-specific scaling behavior of unfolded proteins under folding conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

From minimum-viable-products to full models: a step-wise development of diagnostic forward models in support of design, analysis and modelling on the ST40 tokamak

Like most magnetic confined fusion experiments, the ST40 tokamak started off with a small subset of diagnostics and gradually increased the diagnostic set to include more complex and comprehensive systems. To make the most of each operational phase, forward models of various diagnostics are used and developed to aid design, provide consistency-checks during commissioning, test analysis methods, and build workflows to constrain high-level parameters to inform interpretation, theory and modelling. For new models and new analysis workflows, minimum-viable-products are released early, and their complexity is increased in a step-wise manner, facilitating the support of all programme phases on multiple parallel applications, while enabling learning opportunities and feedback loops. In this contribution we review the philosophy, scope and architecture of the framework under development. We discuss the details of some forward models, with examples on how they are used to aid diagnostic design, to investigate analysis methodologies through synthetic data, and how they are embedded in experimental analysis workflows. We compare previously published experimental results with new, more advanced analysis workflows employing more recent, detailed models and new diagnostic data, providing confirmation of the published material from the 2021–22 experimental campaign.

integrated data analysis↗
DOE OSTI2023

Search for photons above 10 19 eV with the surface detector of the Pierre Auger Observatory

We use the surface detector of the Pierre Auger Observatory to search for air showers initiated by photons with an energy above 10 19 eV. Photons in the zenith angle range from 30° to 60° can be identified in the overwhelming background of showers initiated by charged cosmic rays through the broader time structure of the signals induced in the water-Cherenkov detectors of the array and the steeper lateral distribution of shower particles reaching ground. Applying the search method to data collected between January 2004 and June 2020, upper limits at 95% CL are set to an E -2 diffuse flux of ultra-high energy photons above 10 19 eV, 2 × 10 19 eV and 4 × 10 19 eV amounting to 2.11 × 10 -3 , 3.12 × 10 -4 and 1.72 × 10 -4 km -2 sr -1 yr -1 , respectively. While the sensitivity of the present search around 2 × 10 19 eV approaches expectations of cosmogenic photon fluxes in the case of a pure-proton composition, it is one order of magnitude above those from more realistic mixed-composition models. The inferred limits have also implications for the search of super-heavy dark matter that are discussed and illustrated.

ultra high energy photons and neutrinos↗
DOE OSTI2024

Nonlinear gyrokinetic modelling of high confinement negative triangularity plasmas

Abstract Nonlinear gyrokinetic simulations correctly predict particle as well as ion and electron energy fluxes of high confinement plasmas with a negative triangularity cross sectional shape, showing that core transport in these plasmas is well described by standard gyrokinetic models. Experimentally inferred power balance fluxes are mostly reproduced within one standard deviation across a wide portion of the minor radius. Experimental conditions are reproduced by ion scale simulations, without the need to include density and temperature profile curvature effects. The experimental case is used as baseline to predict that the non-dimensional confinement scaling in negative triangularity plasmas increases strongly with plasma current while slightly degrading at increasing normalized pressure and decreasing collisionality. Recent experiments showed that low toroidal rotation negatively impacts confinement; consistent with the experiment, simulations predict that low rotational shear significantly affects confinement unless the plasma effective charge is maintained above a minimum level. Core confinement is predicted to significantly degrade in low aspect ratio devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Hydra: computer vision for data quality monitoring

Hydra, initially developed for Hall-D in 2019, is a system that utilizes computer vision to perform near real time data quality monitoring. Since then, it has been deployed across all experimental halls at Jefferson Lab, with the CLAS12 collaboration in Hall-B being the first outside of GlueX to fully utilize Hydra. The system comprises back end processes that manage the models, their inferences, and the data flow. Finally, the front-end components, accessible via web pages, allow detector experts and shift crews to view and interact with the system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

OrthoPhyl—streamlining large-scale, orthology-based phylogenomic studies of bacteria at broad evolutionary scales

Abstract There are a staggering number of publicly available bacterial genome sequences (at writing, 2.0 million assemblies in NCBI's GenBank alone), and the deposition rate continues to increase. This wealth of data begs for phylogenetic analyses to place these sequences within an evolutionary context. A phylogenetic placement not only aids in taxonomic classification but informs the evolution of novel phenotypes, targets of selection, and horizontal gene transfer. Building trees from multi-gene codon alignments is a laborious task that requires bioinformatic expertise, rigorous curation of orthologs, and heavy computation. Compounding the problem is the lack of tools that can streamline these processes for building trees from large-scale genomic data. Here we present OrthoPhyl, which takes bacterial genome assemblies and reconstructs trees from whole genome codon alignments. The analysis pipeline can analyze an arbitrarily large number of input genomes (>1200 tested here) by identifying a diversity-spanning subset of assemblies and using these genomes to build gene models to infer orthologs in the full dataset. To illustrate the versatility of OrthoPhyl, we show three use cases: E. coli/Shigella, Brucella/Ochrobactrum and the order Rickettsiales. We compare trees generated with OrthoPhyl to trees generated with kSNP3 and GToTree along with published trees using alternative methods. We show that OrthoPhyl trees are consistent with other methods while incorporating more data, allowing for greater numbers of input genomes, and more flexibility of analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multi-Omics Reveals Temporal Scales of Carbon Metabolism in Synechococcus Elongatus PCC 7942 Under Light Disturbance

Central carbon metabolism in model cyanobacteria involves multiple pathways to adapt to energy-light limitations across diel cycles. However, the success in mechanistic modeling for phenotypic prediction of the protein regulators in the metabolic state depends on capturing the vast possibilities emerging from multiple regulatory pathways in complex biological processes. Here, we developed a physics-informed machine learning approach based on energy-landscape concepts to predict regulatory proteins responding to cyclic circadian and unforeseen light perturbations in cyanobacterial metabolic networks. Our approach provides interpretable de novo models for inferring gene expression dynamics from Synechococcus elongatus over diel cycles and using redox proteome analysis to distinguish immediate light-responsive elements from circadian-regulated processes in carbon metabolism pathways. We identified distinct temporal signatures with the analysis of the redox proteome: there was an immediate shift in cysteine redox states accompanied by a limited change in protein abundance under constant illumination and after 2 hours of darkness. This discovery indicates that the generation of reductants coordinates photoinduced electron transport with redox metabolic pathways in two discernable molecular mechanisms: fast redox-based protein modifications occur immediately after the light disturbance, followed by slow transcriptional regulations across networks. This temporal regulation reveals how metabolic networks integrate rapid light responses with programmed circadian rhythms to maintain cellular homeostasis under the light-energy limitations over the diel cycle.

Biomolecular & subcellular processes↗
DOE OSTI2022

Constraining the galaxy-halo connection of infrared-selected unWISE galaxies with galaxy clustering and galaxy-CMB lensing power spectra

We present the first detailed analysis of the connection between galaxies and their dark matter halos for the unWISE galaxy catalog—a full-sky, infrared-selected sample built from WISE data, containing over 500 million galaxies. Using unWISE galaxy-galaxy autocorrelation and Planck CMB lensing-galaxy cross-correlation measurements down to 10 arcmin angular scales, we constrain the halo occupation distribution (HOD), a model describing how central and satellite galaxies are distributed within dark matter halos, for three unWISE galaxy samples at mean redshifts z ¯ ≈ 0.6 , 1.1, and 1.5, assuming a fixed cosmology at the best-fit Planck Λ CDM values. We constrain the characteristic minimum halo mass to host a central galaxy, M min HOD = 1.8 3 - 1.63 + 0.41 × 10 12 M ⊙ / h , 5.22 - 4.80 + 0.34 × 10 12 M ⊙ / h , 6.60 - 1.11 + 0.30 × 10 13 M ⊙ / h and the mass scale at which one satellite galaxy per halo is found, M 1 ' = 1.1 3 - 0.70 + 0.32 × 10 13 M ⊙ / h , 1.18 - 1.11 + 0.30 × 10 13 M ⊙ / h , 1.23 - 1.17 + 0.14 × 10 14 M ⊙ / h for the unWISE samples at z ¯ ≈ 0.6 , 1.1, and 1.5, respectively. We find that all three samples are dominated by central galaxies, rather than satellites. Using our constrained HOD models, we infer the effective linear galaxy bias for each unWISE sample, and find that it does not evolve as steeply with redshift as found in previous perturbation-theory-based analyses of these galaxies. We discuss possible sources of systematic uncertainty in our results, the most significant of which is the uncertainty on the galaxy redshift distribution. Our HOD constraints provide a detailed, quantitative understanding of how the unWISE galaxies populate the underlying dark matter halo distribution. These constraints will have a direct impact on future studies employing the unWISE galaxies as a cosmological and astrophysical probe, including measurements of ionized gas thermodynamics and dark matter profiles via Sunyaev-Zel’dovich and lensing cross-correlations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Intergalactic magnetic field studies by means of γ -ray emission from GRB 190114C

The presence of delayed GeV emission after a strong transient, such as a gamma-ray burst (GRB), in the very-high energy (VHE) E > 100 GeV band can be the signature of a nonzero magnetic field in the intergalactic medium. Here we used a synchrotron self-Compton multiwavelength model to infer an analytical description of the intrinsic VHE spectrum (corrected for absorption by the extragalactic background light) of GRB 190114C to predict the light curves and spectral energy distributions of the delayed emission with Monte Carlo simulations for different intergalactic magnetic field (IGMF) configurations (strengths B = 8 × 10 -21 G, 10 -20 G, 3 × 10 -20 G and correlation length $\lambda$ > 1 Mpc), and compared them with the Fermi Large Area Telescope (Fermi LAT) limits computed for several exposure times. We found that the Fermi LAT is not sensitive enough to constrain any IGMF strengths using GRB 190114C.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Evolving Energy Efficient Convolutional Neural Networks

As deep neural networks have been deployed in more and more applications over the past half decade and are finding their way into an ever increasing number of operational systems, their energy consumption becomes a concern whether running in the datacenter or on edge devices. Hyperparameter optimization and automated network design for deep learning is a quickly growing field, but much of the focus has remained only on optimizing for the performance of the machine learning task. In this work, we demonstrate that the best performing networks created through this automated network design process have radically different computational characteristics (e.g. energy usage, model size, inference time), presenting the opportunity to utilize this optimization process to make deep learning networks more energy efficient and deployable to smaller devices. Optimizing for these computational characteristics is critical as the number of applications of deep learning continues to expand.

Young, Steven↗
DOE OSTI2019

Scalable Extended Dynamic Mode Decomposition Using Random Kernel Approximation

The Koopman operator is a linear, infinite-dimensional operator that governs the dynamics of system observables; extended dynamic mode decomposition (EDMD) is a data-driven method for approximating the Koopman operator using functions (features) of the system state snapshots. This paper investigates an approach to EDMD in which the features used provide random approximations to a particular kernel function. The objective of this is computational economy for large data sets: EDMD is generally ill-suited for problems with large state dimension, and its dual kernel formulation (KDMD) is well-suited for such problems only if the number of data snapshots is relatively small. We discuss two specific methods for generating features: random Fourier features and the Nÿstrom method. The first method is a data-independent method for translation-invariant kernels only and involves random sampling in feature space; the second method is a data-dependent empirical method that may be used for any kernel and involves random sampling of data. We first discuss how these ideas may be applied in an EDMD context, as well as a means for adaptively adding random Fourier features. As a result, we demonstrate these methods on two example problems and conclude with an analysis of the relative benefits and drawbacks of each method.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Performance Study of CXL Memory Topology

This paper presents a comprehensive evaluation of the performance impact of various Compute Express Link (CXL) memory topologies, with a particular emphasis on CXL switches, in the context of High- Performance Computing (HPC) and Large Language Model (LLM) inference workloads. Our study unveils significant performance variations across different topologies, demonstrating that certain configurations yield superior performance for specific workloads. These findings underscore the critical importance of tailored topol- ogy selection in optimizing system performance. Additionally, we address the inherent challenges associated with integrating CXL switches, including overhead considerations and routing complex- ities. Our research highlights the necessity for thorough evalua- tion methodologies to fully leverage CXL technology’s potential in contemporary computing environments. These insights provide valuable guidance for system architects and data center operators in designing and optimizing CXL-based infrastructures for diverse workload requirements.

CXL, memory, Artificial Intelligence (AI), HPC↗
DOE OSTI2022

Contributing to Sunny open source code

The Sunny package is an existing open source software to support the simulation of magnetic spin systems. It is available online under the MIT license here: https://github.com/SunnySuite/Sunny.jl The package aims to include the following features: - Analysis for symmetry-allowed interactions - Monte Carlo algorithms for simulating equilibrium configurations. - Schemes for integrating spin dynamics. - Inference of model Hamiltonians - Support for generalized SU(N) spins - Tools for interpreting experimental data, such as diffraction data, magnetization curves, and heat capacity data

Barros, Kipton↗
DOE OSTI2026

Simulation-Based Inference for Neutrino Parameter Tuning

This code trains a simulation-based inference (SBI) model for neutrino interaction parameter tuning and subsequently evaluates its performance on MicroBooNE, T2K, and NuWro datasets.

Tame-Narvaez, Karla [Fermi National Accelerator La↗
DOE OSTI2021

2020 ETI Annual Summer School: Data Science and Engineering

The Consortium for Enabling Technologies & Innovation (ETI) was established in 2019 to address emerging technologies within the context of nuclear nonproliferation. ETI creates a research and education environment to support cross-cutting technologies across three core disciplines: 1) computer and engineering science research specifically in a form of machine learning and high performance computing (HPC), 2) advanced manufacturing, and 3) nuclear detection technologies. For outreach and development, ETI hosted the first of three summer schools from August 24-28, 2020 with the theme of “Data Science and Engineering”. The school was hosted in an on-line format and had over 200 participants. The recorded content is available on-line as a resource for students. The summer school had four modules: 1) Fundamentals of data Applications, 2) Computational Machine Learning, 3) Bayesian Modeling and Inference, and 4) Data Science for Safeguards. Modules contained both lectures as well as student exercises. Poll Everywhere was utilized in some modules as an on-line method to engage large groups of students. Upcoming ETI Summer Schools include Novel Instrumentation in 2021 and Advanced Manufacturing in 2022.

Biegalski, Steven R.↗
DOE OSTI2022

Semantic Regression for Disentangling Beam Losses in the Fermilab Main Injector and Recycler

Fermilab’s Main Injector enclosure houses two accelerators: the Main Injector (MI) and the Recycler (RR). In periods of joint operation, when both machines contain high intensity beam, radiative beam losses from MI and RR overlap on the enclosure’s beam loss monitoring (BLM) system, making it difficult to attribute those losses to a single machine. Incorrect diagnoses result in unnecessary downtime that incurs both financial and experimental cost. In this work, we introduce a novel neural approach for automatically disentangling each machine’s contributions to those measured losses. Using a continuous adaptation of the popular UNet architecture in conjunction with a novel data augmentation scheme, our model accurately infers the machine of origin on a per-BLM basis in periods of joint and independent operation. Crucially, by extracting beam loss information at varying receptive fields, the method is capable of learning both local and global machine signatures and producing high quality inferences using only raw BLM loss measurements.

43 PARTICLE ACCELERATORS↗