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DOE OSTI2023

Genomic variation across Chinook salmon populations reveals effects of a duplication on migration alleles and supports fine scale structure

Abstract The distribution of ecotypic variation in natural populations is influenced by neutral and adaptive evolutionary forces that are challenging to disentangle. This study provides a high‐resolution portrait of genomic variation in Chinook salmon ( Oncorhynchus tshawytscha ) with emphasis on a region of major effect for ecotypic variation in migration timing. With a filtered data set of ~13 million single nucleotide polymorphisms (SNPs) from low‐coverage whole genome resequencing of 53 populations (3566 barcoded individuals), we contrasted patterns of genomic structure within and among major lineages and examined the extent of a selective sweep at a major effect region underlying migration timing (GREB1L/ROCK1). Neutral variation provided support for fine‐scale structure of populations, while allele frequency variation in GREB1L/ROCK1 was highly correlated with mean return timing for early and late migrating populations within each of the lineages ( r 2 = .58–.95; p < .001). However, the extent of selection within the genomic region controlling migration timing was much narrower in one lineage (interior stream‐type) compared to the other two major lineages, which corresponded to the breadth of phenotypic variation in migration timing observed among lineages. Evidence of a duplicated block within GREB1L/ROCK1 may be responsible for reduced recombination in this portion of the genome and contributes to phenotypic variation within and across lineages. Lastly, SNP positions across GREB1L/ROCK1 were assessed for their utility in discriminating migration timing among lineages, and we recommend multiple markers nearest the duplication to provide highest accuracy in conservation applications such as those that aim to protect early migrating Chinook salmon. These results highlight the need to investigate variation throughout the genome and the effects of structural variants on ecologically relevant phenotypic variation in natural species.

Horn, Rebekah L.↗

DOE OSTI2024

Reduced‐Order Modeling for Linearized Representations of Microphysical Process Rates

Abstract Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Physical Principles of Self-Consistent Simulation of the Generation of Interface States and the Transport of Hot Charge Carriers in Field-Effect Transistors Based on Metal–Oxide–Semiconductor Structures

A detailed simulation of degradation (caused by hot charge carriers) based on self-consistent consideration of the transport of charge carriers and the generation of defects at the SiO{sub 2}/Si interface is carried out for the first time. The model is tested using degradation data obtained with decananometer n-type-channel field-effect transistors. It is shown that the mutual influence of the above aspects is significant and their independent simulation gives rise to considerable quantitative errors. In calculations of the energy distribution for charge carriers, the actual band structure of silicon and such mechanisms as impact ionization, scattering at an ionized impurity, and also electron–phonon and electron–electron interactions are taken into account. At the microscopic level, the generation of defects is considered as the superposition of single-particle and multiparticle mechanisms of breakage of the Si–H bond. A very important applied aspect of this study is the fact that our model makes it possible to reliably assess the operating lifetime of a transistor subjected to the effects of “hot” charge carriers.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Extended range X-ray pair distribution functions

Here we describe a dual detector system for high-energy X-ray, simultaneous, small and wide-angle X-ray scattering (SAXS and WAXS), designed to extract extended-range pair distribution functions (ER-PDF) for disordered materials. The hardware and software provides continuous reciprocal space coverage over atomic to nanometer length-scales. Details of the varying resolution, splicing of data and normalization on an absolute scale are outlined. In addition, the combination of SAXS and WAXS theory is considered with a view to enabling a direct Fourier transformation of the structure factor spanning multiple length-scales into real space. Important distinctions between the ER-PDF and the pair distance distribution function (PDDF) representations are demonstrated. It is shown that when the SAXS intensity in the structure factor, S(Q), is similar to the WAXS intensity, the contributions to the ER-PDF are minimal. However, when the SAXS S(Q) intensities are substantially stronger than the WAXS, the ER-PDF can provide important structural information on the local, intermediate and nanometer length-scales. Notably, the ER-PDF method provides direct information on particle sizes and their density distributions, overcoming the limitations of PDDF analysis for densely packed systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗

DOE OSTI2022

A New Member of the Milky Way’s Family Tree: Characterizing the Pontus Merger of Our Galaxy

We study the Pontus structure—a recently discovered merger that brought in ~7 globular clusters in the course of the hierarchical buildup of the Milky Way’s halo. Here, we analyze the stellar population of Pontus and examine (1) its phase-space distribution using the ESA/Gaia data set, (2) its metallicity and chemical abundances (i.e., [Fe/H], [ α /Fe], [Mg/Fe], and [Al/Fe]) using the spectroscopic catalog of APOGEE DR17, and (3) the color–magnitude diagram that shows interesting features, including a possibly double horizontal branch and a small population of blue stragglers. In sum, the Pontus stars show some unique properties that suggest they likely originated from the merging of an independent satellite galaxy; however, future analysis will shed more light on the true nature of this structure. This chemodynamical analysis of Pontus stars is another step forward in our bigger quest to characterize all the merging events of our Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Joint Strong and Weak Lensing Analysis of the Massive Cluster Field J0850+3604

In this work, we present a combined strong and weak lensing analysis of the J085007.6+360428 (J0850) field, which contains the massive cluster Zwicky 1953. This field was selected for its high projected concentration of luminous red galaxies. Using Subaru/Suprime-Cam ${{BVR}}_{c}{I}_{c}{i}^{\prime }{z}^{\prime }$ imaging and MMT/Hectospec spectroscopy, we first perform a weak lensing shear analysis to constrain the mass distribution in this field, including the cluster at z = 0.3774 and a smaller foreground halo at z = 0.2713. We then add a strong lensing constraint from a multiply imaged galaxy in the imaging data with a photometric redshift of z ≈ 5.03. Unlike previous cluster-scale lens analyses, our technique accounts for the full three-dimensional mass structure in the beam, including galaxies along the line of sight. In contrast with past cluster analyses that used only lensed image positions as constraints, we use the full surface brightness distribution of the images. This method predicts that the source galaxy crosses a lensing caustic, such that one image is a highly magnified "fold arc" that could be used to probe the source galaxy's structure at ultra-high spatial resolution (<30 pc). We calculate the mass of the primary cluster to be ${M}_{\mathrm{vir}}={2.93}_{-0.65}^{+0.71}\times {10}^{15}\,{M}_{\odot }$ with a concentration of ${c}_{\mathrm{vir}}={3.46}_{-0.59}^{+0.70}$, consistent with the mass–concentration relation of massive clusters at a similar redshift. Finally, the large mass of this cluster makes J0850 an excellent field for leveraging lensing magnification to search for high-redshift galaxies, competitive with and complementary to that of well-studied clusters such as the HST Frontier Fields.

79 ASTRONOMY AND ASTROPHYSICS↗

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 OSTI2017

Grid site availability evaluation and monitoring at CMS

The Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) uses distributed grid computing to store, process, and analyse the vast quantity of scientific data recorded every year. The computing resources are grouped into sites and organized in a tiered structure. Each site provides computing and storage to the CMS computing grid. Over a hundred sites worldwide contribute with resources from hundred to well over ten thousand computing cores and storage from tens of TBytes to tens of PBytes. In such a large computing setup scheduled and unscheduled outages occur continually and are not allowed to significantly impact data handling, processing, and analysis. Unscheduled capacity and performance reductions need to be detected promptly and corrected. CMS developed a sophisticated site evaluation and monitoring system for Run 1 of the LHC based on tools of the Worldwide LHC Computing Grid. For Run 2 of the LHC the site evaluation and monitoring system is being overhauled to enable faster detection/reaction to failures and a more dynamic handling of computing resources. Furthermore, enhancements to better distinguish site from central service issues and to make evaluations more transparent and informative to site support staff are planned.

43 PARTICLE ACCELERATORS↗

DOE OSTI2018

SHAD: the Scalable High-performance Algorithms and Data-structures Library

The unprecedented amount of data that needs to be processed in emerging data analytics applications poses novel challenges to industry and academia. Scalability and high performance become more than a desirable feature because, due to the scale and the nature of the problems, they draw the line between what is achievable and what is unfeasible. In this paper, we propose SHAD, the Scalable High-performance Algorithms and Data-structures library. SHAD adopts a modular design that confines low level details and promotes reuse. SHAD's core is built on an Abstract Runtime Interface which enhances portability and identifies the minimal set of features of the underlying system required by the framework. The core library includes common data-structures such as: Array, Vector, Map and Set. These are designed to accommodate significant amount of data which can be accessed in massively parallel environments, and used as building blocks for SHAD extensions, i.e. higher level software libraries. We have validated and evaluated our design with a performance and scalability study of the core components of the library. We have validated the design flexibility by proposing a Graph Library as an example of SHAD extension, which implements two different graph data-structures; we evaluate their performance with a set of graph applications. Experimental results show that the approach is promising in terms of both performance and scalability. On a distributed system with 320 cores, SHAD Arrays are able to sustain a throughput of 65 billion operations per second, while SHAD Maps sustain 1 billion of operations per second. Algorithms implemented using the Graph Library exhibit performance and scalability comparable to a custom solution, but with smaller development effort.

Castellana, Vito G.↗

DOE OSTI2019

Structural Dynamic Analysis of Spent Nuclear Fuel

This unlimited distribution milestone report is the deliverable for M2SF-19PN010202014. This report completes the modeling and analysis related to the Equipos Nucleares Sociedad Anónima/U.S. Department of Energy (ENSA/DOE) spent nuclear fuel (SNF) multimodal transportation test (MMTT) campaign performed in 2017. A previous report studied the shock and vibration data generated during the MMTT and determined effective modeling and analysis methodologies and strategies. This report completes the modeling and analysis activity by applying the structural dynamic analysis models and methods to the Atlas railcar system. This analysis demonstrates that the MMTT configuration provided a conservative, bounding shock and vibration environment for SNF in comparison to finite element model results that are configured to represent the Atlas railcar system and all of its SNF package combinations. Additionally, this report establishes a technical basis for recommending that no further analysis is necessary to demonstrate that SNF cladding will remain intact during shock and vibration loading conditions that occur during normal conditions of transportation (NCT). 10 CFR 71.71 defines package tests to demonstrate acceptable package performance under NCT conditions. The focus of this work is to demonstrate that SNF cladding will remain intact within the package while it is subjected to what 10 CFR 71.71 calls: Vibration normally incident to transport. The MMTT recorded negligible shock and vibrations on the SNF cladding during the test campaign, and this analysis supports the conclusion that intact fuel rods carried in any current SNF transportation package will experience similar, negligible, shock and vibration conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

A Framework to Demonstrate a DNP3 Interface With a CIM-Based Data Integration Platform: Preprint

The contemporary electrical grid is characterized by its complexity and abundance of data. A control-rich environment supported by information and communication technologies within an Advanced Distribution Management System (ADMS) presents a viable and cost-effective option for utility companies aiming to implement advanced real-time analytical schemes for monitoring and remotely controlling distribution feeders. Modular platform-based approaches to distribution operations require a structured framework for acquiring field device measurements, performing analytics, converting the setpoint to the correct protocol, and sending it on the appropriate communications network to the field devices. We present the development and deployment of an application service to integrate an open-source standardsbased platform with an ADMS test bed with field devices using the Distributed Network Protocol (DNP3) for data exchange. The step-by-step procedure for establishing the DNP3-Master service on an open-source distribution platform is outlined, comprehensively explaining the Master setup process. Moreover, sample use case results highlight the capabilities of the DNP3- Master service setup. Results demonstrate the scalability and configurability of the DNP3-Master service, making it adaptable for integration with other relevant applications, thus providing potential opportunities for real-world field trials and real-time assessments.

ADMS↗

DOE OSTI2019

A Semi-Empirical Framework for Interpreting Traveling Wave Ion Mobility Arrival Time Distributions

The inherent structural heterogeneity of biomolecules is an important biophysical property that is essential to their function, but is challenging to characterize experimentally. We present a workflow that rapidly and quantitatively assesses the conformational heterogeneity of peptides and proteins in the gas phase using traveling wave ion mobility (TWIM) arrival time distributions (ATDs). We have established a set of semi-empirical equations that model the TWIM ATD peak width and resolution across a wide range of wave amplitudes (V) and wave velocities (v). In addition, a conformational broadening parameter, δ, can be extracted from this analysis that reports on the contribution of conformational heterogeneity to the broadening of TWIM ATD peak width during ion mobility separation. We use this δ value to evaluate the conformational heterogeneity of a set of helical peptides, and our analysis correlates well with previous peak width observations reported for these ions. Furthermore, we use molecular dynamics simulations to independently investigate the general flexibility of these peptides in the gas phase, and generate similar trends found in experimental TWIM data. Finally, we extended our analysis to Avidin, a 64-kDa homotetramer, and quantify the structural heterogeneity of this intact complex using TWIM ATD data as a function of cross-linking. We observe an initial reduction in δ values as a function of cross-linker concentration, demonstrating the sensitivity of our δ value analysis to changes in flexibility of the assembly. .

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2019

Status of the experimental studies on DVMP and transversity GPDs

The cross section of the exclusive $\pi^0$ and $\eta$ electroproduction reaction $ep\to e^\prime p^\prime \pi^0/\eta$ was measured at Jefferson Lab with a 5.75-GeV electron beam and the CLAS detector. Differential cross sections $d^4\sigma/dtdQ^2dx_Bd\phi$ and structure functions $\sigma_U = \sigma_T+\epsilon\sigma_L, \sigma_{TT}$ and $\sigma_{LT}$, as functions of $t$ were obtained over a wide range of $Q^2$ and $x_B$. At low $t$, both $\pi^0$ and $\eta$ are described reasonably well by Generalized Parton Distributions (GPDs) in which chiral-odd transversity GPDs are dominant. Generalized form factors of the transversity GPDs $\langle H_T \rangle^{\pi,\eta}$ and $\langle \bar{E}_T \rangle^{\pi,\eta}$ were directly extracted from the experimental observables. The combined $\pi^0$ and $\eta$ data opens the way for the flavor decomposition of the transversity GPDs. The first ever demonstration of this decomposition was done for the transversity GPDs $H_T$ and $\bar E_T$. GPD $\bar E_T$ is connected with the density of the polarized quarks in an unpolarized nucleon in the impact parameter space. The spin density of polarized $u$ and $d$-quarks was evaluated for different values of Feynman $x$ from the GPD model tuned to described the experimental data.

Kubarovsky, Valery↗

DOE OSTI2020

Seasonal representation of extreme precipitation indices over the United States in CMIP6 present-day simulations

Realistically representing the present-day characteristics of extreme precipitation has been a challenge for global climate models, which is due in part to deficiencies in model resolution and physics, but is also due to a lack of consistency in gridded observations. In this study, we use three observation datasets, including gridded rain gauge and satellite data, to assess historical simulations from sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We separately evaluate summer and winter precipitation over the United States (US) with a comprehensive set of extreme precipitation indices, including an assessment of precipitation frequency, intensity and spatial structure. The observations exhibit significant differences in their estimates of area-average intensity distributions and spatial patterns of the mean and extremes of precipitation over the US. In general, the CMIP6 multi-model mean performs better than most individual models at capturing daily precipitation distributions and extreme precipitation indices, particularly in comparison to gauge-based data. Also, the representation of the extreme precipitation indices by the CMIP6 models is better in the summer than winter. Although the ‘standard’ horizontal-resolution can vary significantly across CMIP6 models, from ~0.7° to ~2.8°, we find that resolution is not a good indicator of model performance. Overall, our results highlight common biases in CMIP6 models and demonstrate that no single model is consistently the most reliable across all indices.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Small-angle X-ray and neutron scattering

Small-angle scattering (SAS) is a technique that is able to probe the structural organization of matter and quantify its response to changes in external conditions. X-ray and neutron scattering profiles measured from bulk materials or materials deposited at surfaces arise from nanostructural inhomogeneities of electron or nuclear density. Furthermore, the analysis of SAS data from coherent scattering events provides information about the length scale distributions of material components. Samples for SAS studies may be prepared in situ or under near-native conditions and the measurements performed at various temperatures, pressures, flows, shears or stresses, and in a time-resolved fashion. In this Primer, we provide an overview of SAS, summarizing the types of instrument used, approaches for data collection and calibration, available data analysis methods, structural information that can be obtained using the method, and data depositories, standards and formats. Recent applications of SAS in structural biology and the soft-matter and hard-matter sciences are also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Superclustering with the Atacama Cosmology Telescope and Dark Energy Survey. I. Evidence for Thermal Energy Anisotropy Using Oriented Stacking

The cosmic web contains filamentary structure on a wide range of scales. On the largest scales, superclustering aligns multiple galaxy clusters along intercluster bridges, visible through their thermal Sunyaev–Zel'dovich signal in the cosmic microwave background. We demonstrate a new, flexible method to analyze the hot gas signal from multiscale extended structures. We use a Compton y-map from the Atacama Cosmology Telescope (ACT) stacked on redMaPPer cluster positions from the optical Dark Energy Survey (DES). Cutout images from the y-map are oriented with large-scale structure information from DES galaxy data such that the superclustering signal is aligned before being overlaid. We find evidence of an extended quadrupole moment of the stacked y signal at the 3.5σ level, demonstrating that the large-scale thermal energy surrounding galaxy clusters is anisotropically distributed. We compare our ACT × DES results with the Buzzard simulations, finding broad agreement. Using simulations, we highlight the promise of this novel technique for constraining the evolution of anisotropic, non-Gaussian structure using future combinations of microwave and optical surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense , a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Computer Science↗