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At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Baycal

Bayesian Model Calibration (BayCal) toolkit is a software plugin for Risk Analysis Virtual Environment (RAVEN) framework, arming at inversely quantifying the uncertainties associated with simulation model parameters based on available experiment data. BayCal seeks statistical inference of the uncertain input parameters that are consistent with the available measurement data or observed data. The unique feature of BayCal is the capability to be linked with RAVEN to build corresponding calibration workflows for complex multi-physics simulations. In addition to be able to use the machine learning capability of RAVEN to significantly reduce the computational cost of expensive simulation models, another distinctive feature of BayCal is the capability to deal with high-dimensional correlated model outputs, such as time series observations at multiple locations, via principal component analysis (PCA) technique.

Wang, Congjian↗
DOE OSTI2020

Reconstructing gravity on cosmological scales

We present the data-driven late-time reconstruction of gravitational theories and dark energy models on cosmological scales. We showcase the power of present cosmological probes at constraining these models and quantify the knowledge of their properties that can be acquired through state of the art data. This reconstruction exploits the power of the effective field theory approach to dark energy and modified gravity phenomenology, which compresses the freedom in defining such models into a finite set of functions that can be reconstructed across cosmic times using cosmological data. We consider several model classes described within this framework and thoroughly discuss their phenomenology and data implications. In this work, we find that some models can alleviate the present discrepancy in the determination of the Hubble constant as inferred from the cosmic microwave background and as directly measured. This results in a statistically significant preference for the reconstructed theories over the standard cosmological model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics

In this Letter, we present a framework that combines machine learning potential (MLP) and metadynamics to investigate solid-solid phase transition. Here, based on the spectral descriptors and neural networks regression, we develop a scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying it to the simulation of B4–B1 phase transition of GaN under 50 GPa with different model sizes, we observe sequential change of the phase transition mechanism from collective modes to nucleation and growths. When the size is at or below 128 000 atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nuclei occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of the atomistic mechanism manifests the importance of statistical sampling with large system size in phase transition modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Impact of Non-Steady State Operation on Cooling Water Consumption at Coal- and Natural Gas-Fired Power Plants

Increased renewable energy penetration in the electricity grid in coming decades will result in more frequent cycling at thermal power plants. Simultaneously, thermal power plants may face water scarcity with declining availability of cooling water. Therefore, to enhance thermal power plants’ resiliency to water shortages it is important to understand how non-steady conditions due to cycling will impact cooling water consumption and withdrawal intensity. Non-steady operation at power plants has been previously shown to decrease power plant thermal efficiency. Energy balance models have also demonstrated that a decrease in thermal efficiency is expected to increase cooling water consumption intensity. Furthermore, past work has used operating hours data to show idling and cycling gaps where cooling systems operate more than corresponding generators. As a result, an increase in cycling behavior may impact cooling water consumption and withdrawal intensity. This study uses data from the Energy Information Administration (EIA) and Environmental Protection Agency (EPA) to quantify the impact of cycling cooling water consumption intensity for recirculating cooling systems and withdrawal intensity for once-through cooling systems using energy balance and statistical approaches. In a novel application of a fixed effects model to study the effect of temperature on cooling water consumption and withdrawal intensity, this study finds temperature was consistently expected to increase consumption intensity and withdrawal intensity. Non-steady state conditions do not increase cooling water consumption intensity with statistical significance across unit types. However, additional validation of cooling water data is required to confirm these observed trends due to the sensitivity of these findings to model form and concerns with data quality of the dependent variables, cooling water consumption and withdrawal.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks. Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Bridging the Gap between Cosmological Simulations with Graph Neural Networks and Domain Adaptation

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross dataset tasks (up to 28% better relative error and up to almost an order of magnitude better χ 2 ). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Studies of J/ψ production at forward rapidity in Pb–Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV

The inclusive J/ψ production in Pb–Pb collisions at the center-of-mass energy per nucleon pair $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV, measured with the ALICE detector at the CERN LHC, is reported. The J/ψ meson is reconstructed via the dimuon decay channel at forward rapidity (2.5 < y < 4) down to zero transverse momentum. The suppression of the J/ψ yield in Pb–Pb collisions with respect to binary-scaled pp collisions is quantified by the nuclear modification factor (R AA ). The R AA at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV is presented and compared with previous measurements at $\sqrt{s_{\mathrm{NN}}}$= 2.76 TeV as a function of the centrality of the collision, and of the J/ψ transverse momentum and rapidity. The inclusive J/ψ R AA shows a suppression increasing toward higher transverse momentum, with a steeper dependence for central collisions. The modification of the J/ψ average transverse momentum and average squared transverse momentum is also studied. Comparisons with the results of models based on a transport equation and on statistical hadronization are carried out.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Graph neural networks for efficient learning of mechanical properties of polycrystals

Herein we present graph neural networks (GNNs) as an efficient and accurate machine learning approach to predict mechanical properties of polycrystalline materials. Here, a GNN was developed based on graph representation of polycrystals incorporating only fundamental features of grains including their crystallographic orientation, size, and grain neighbor connectivity information. We tested our method on modeling stiffness and yield strength of α -Ti microstructures, varying in their crystallographic texture. We find the GNN predicts both properties with high accuracy with mean relative errors of ~1% for unseen microstructures from a given set of textures and < 2% for microstructures of unseen texture, even when presented with limited training data. This accuracy is comparable to methods that require high-resolution three-dimensional (3D) microstructure data, such as 3D convolutional neural networks (3D-CNNs) and models that depend on the computation of spatial statistics. The present results show that graph-based deep learning is a promising framework for property prediction, especially considering the high cost associated with obtaining high-resolution 3D microstructure data and the general scarcity of experimental materials datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Search for a boosted Higgs boson decaying to bottom quark pairs in association with a W or Z boson in proton-proton collisions at $\sqrt{s}$ = 13TeV

A search is conducted for standard model Higgs bosons with large transverse momentum ($p_\mathrm{T}$) decaying to bottom quark pairs and produced in association with a hadronically decaying W or Z boson at the LHC. The result is based on a dataset of proton-proton collisions at a center-of-mass energy of 13 TeV collected with the CMS detector in 2016$-$2018, corresponding to an integrated luminosity of 138 fb$^{-1}$. Boosted Higgs, W, and Z boson decays are reconstructed using large-radius jets with $p_\mathrm{T}$$\gt$ 450 GeV and identified with heavy-flavor classifiers based on a graph convolutional neural network. The observed signal strength relative to the standard model expectation is $μ$ = 0.72 $^{+0.75}_{-0.71}$ including statistical and systematic uncertainties.

CMS↗
DOE OSTI2020

Supervised enhancer prediction with epigenetic pattern recognition and targeted validation

Enhancers are important non-coding elements, but they have traditionally been hard to characterize experimentally. The development of massively parallel assays allows the characterization of large numbers of enhancers for the first time. Here, we developed a framework using Drosophila STARR-seq to create shape-matching filters based on meta-profiles of epigenetic features. We integrated these features with supervised machine-learning algorithms to predict enhancers. We further demonstrated that our model could be transferred to predict enhancers in mammals. We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mice and transduction-based reporter assays in human cell lines (153 enhancers in total). The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription factor binding patterns at predicted enhancers versus promoters. Here, we demonstrated that these patterns enable the construction of a secondary model that effectively distinguishes enhancers and promoters.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Dark Energy Camera All Data Everywhere cosmic shear project V: Constraints on cosmology and astrophysics from 270 million galaxies across 13,000 deg$^2$ of the sky

We present constraints on models of cosmology and astrophysics using cosmic shear data vectors from three datasets: the northern and southern Galactic cap of the Dark Energy Camera All Data Everywhere (DECADE) project, and the Dark Energy Survey (DES) Year 3. These data vectors combined consist of 270 million galaxies spread across 13,000 ${\rm deg}^2$ of the sky. We first extract constraints for $Λ$CDM cosmology and find $S_8= 0.805^{+0.019}_{-0.019}$ and $Ω_{\rm m} = 0.262^{+0.023}_{-0.036}$, which is consistent within $1.9 σ$ of constraints from the Planck satellite. Extending our analysis to dynamical dark energy models shows that lensing provides some (but still minor) improvements to existing constraints from supernovae and baryon acoustic oscillations. Finally, we study six different models for the impact of baryons on the matter power spectrum. We show the different models provide consistent constraints on baryon suppression, and associated cosmology, once the astrophysical priors are sufficiently wide. Current scale-cut approaches for mitigating baryon contamination result in a residual bias of $\approx 0.3σ$ in the $S_8, Ω_{\rm m}$ posterior. Using all scales with dedicated baryon modeling leads to negligible improvement as the new information is used solely to self-calibrate the baryon model on small scales. Additional non-lensing datasets, and/or calibrations of the baryon model, will be required to access the full statistical power of the lensing measurements. The combined dataset in this work represents the largest lensing dataset to date (most galaxies, largest area) and provides an apt testing ground for analyses of upcoming datasets from Stage IV surveys. The DECADE shear catalogs, data vectors, and likelihoods are made publicly available.

Anbajagane, D. [Chicago U., Astron. Astrophys. Ctr↗
DOE OSTI2020

Dalitz analysis of $D^0 → K^–π^+η$ decays at Belle

We present the results of the first Dalitz plot analysis of the decay D 0 → K – π + η . The analysis is performed on a data set corresponding to an integrated luminosity of 953 fb – 1 collected by the Belle detector at the asymmetric-energy e + e – KEKB collider. The Dalitz plot is well described by a combination of the six resonant decay channels K ¯ * ( 892 ) 0 η , K – a 0 ( 980 ) + , K – a 2 ( 1320 ) + , K ¯ * ( 1410 ) 0 η , K * ( 1680 ) – π + and K 2 * ( 1980 ) – π + , together with K π and K η S-wave components. The decays K * ( 1680 ) – → K – η and K 2 * ( 1980 ) – → K – η are observed for the first time. We measure ratio of the branching fractions, B ( D 0 → K – π + η ) B ( D 0 → K – π + ) = 0.500 ± 0.002 ( stat ) ± 0.020 ( syst ) ± 0.003 ( B PDG ) . Using the Dalitz fit result, the ratio B ( K * ( 1680 ) → K η ) B ( K * ( 1680 ) → K π ) is measured to be 0.11 ± 0.02 ( stat ) – 0.04 + 0.06 ( syst ) ± 0.04 ( B PDG ) ; this is much lower than the theoretical expectations ( ≈ 1 ) made under the assumption that K * ( 1680 ) is a pure 1 3 D 1 state. The product branching fraction B ( D 0 → [ K 2 * ( 1980 ) – → K – η ] π + ) = ( 2. 2 – 1.9 + 1.7 ) × 10 – 4 is determined. In addition, the π η ' contribution to the a 0 ( 980 ) ± resonance shape is confirmed with 10.1 σ statistical significance using the three-channel Flatté model. We also measure B ( D 0 → K ¯ * ( 892 ) 0 η ) = ( 1.4 1 – 0.12 + 0.13 ) % . This is consistent with, and more precise than, the current world average ( 1.02 ± 0.30 ) % , deviates with a significance of more than 3 σ from the theoretical predictions of (0.51–0.92)%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Measurements of Proton High-Order Cumulants in s N N = 3 GeV Au + Au Collisions and Implications for the QCD Critical Point

We report cumulants of the proton multiplicity distribution from dedicated fixed-target Au + Au collisions at $\sqrt{s_{NN}}$ = 3.0 GeV , measured by the STAR experiment in the kinematic acceptance of rapidity ( y ) and transverse momentum ( p T ) within - 0.5 < y < 0 and 0.4 < pT < 2.0 GeV / c . In the most central 0%–5% collisions, a proton cumulant ratio is measured to be C 4 / C 2 = - 0.85 ± 0.09 ( stat ) ± 0.82 ( syst ) , which is 2 σ below the Poisson baseline with respect to both the statistical and systematic uncertainties. The hadronic transport UrQMD model reproduces our C 4 / C 2 in the measured acceptance. Compared to higher energy results and the transport model calculations, the suppression in C 4 / C 2 is consistent with fluctuations driven by baryon number conservation and indicates an energy regime dominated by hadronic interactions. These data imply that the QCD critical region, if created in heavy-ion collisions, could only exist at energies higher than 3 GeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

The Value of Information From Horizontal Distributed Acoustic Sensing Compared to Multicomponent Geophones Via Machine Learning

Abstract Faults play an important role in recharging many geothermal reservoirs, and seismic information can image the locations of these faults. The value of information (VOI) metric is used to objectively quantify and compare the value of two types of seismic receiver data via a machine learning approach. The demonstrated VOI methodology is novel by including spatial models from seismic data and obtaining the information statistics from machine learning. Our two-dimensional numerical experiments compare images created from sparsely spaced (80 m), two-component geophone sampling to high spatial resolution (1 m), single-component DAS. We used a three-fold cross validation of a U-Net convolutional neural networks to achieve average classification statistics. The results suggest that when horizontal sources are utilized, geophones and DAS identify reflectors and non-reflectors at roughly the same rate. The average F1 score for horizontal DAS is 0.939 and 0.931 for geophones. For images created from a vertical source, DAS performed marginally better (F1 = 0.919) than geophones (F1 = 0.877). Our transferrable methodology can provide guidance on which acquisition scenarios can improve images of important structures in the subsurface and present an efficient method for obtaining reliability statistics from high-dimensional, spatial data.

Energy & Fuels↗
DOE OSTI2026

BRCore: an R package implementing flexible selection of core taxa using contribution to Bray-Curtis dissimilarity and neutral model fitting

Identifying core taxa in microbial ecology highlights groups likely to participate in a broad range of potential ecological interactions. Here, we present BRCore, an R package to identify core taxa using abundance-occupancy distributions and beta-diversity contributions across ecological niches, and predict stochastic and deterministic taxa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Search for squarks and gluinos in final states with one isolated lepton, jets, and missing transverse momentum at $\sqrt{s}=13$ with the ATLAS detector

The results of a search for gluino and squark pair production with the pairs decaying via the lightest charginos into a final state consisting of two W bosons, the lightest neutralinos ($\tilde{χ}^0_1$), and quarks, are presented: the signal is characterised by the presence of a single charged lepton (e ± or μ ± ) from a W boson decay, jets, and missing transverse momentum. The analysis is performed using 139 fb -1 of proton–proton collision data taken at a centre-of-mass energy √s=13 delivered by the Large Hadron Collider and recorded by the ATLAS experiment. No statistically significant excess of events above the Standard Model expectation is found. Limits are set on the direct production of squarks and gluinos in simplified models. Masses of gluino (squark) up to 2.2 (1.4 ) are excluded at 95% confidence level for a light $\tilde{χ}^0_1$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Accelerating LHC phenomenology with analytic one-loop amplitudes: A C++ interface to MCFM

Abstract The evaluation of one-loop matrix elements is one of the main bottlenecks in precision calculations for the high-luminosity phase of the Large Hadron Collider. To alleviate this problem, a new C++ interface to the "Image missing" <#comment/> parton-level Monte Carlo is introduced, giving access to an extensive library of analytic results for one-loop amplitudes. Timing comparisons are presented for a large set of Standard Model processes. These are relevant for high-statistics event simulation in the context of experimental analyses and precision fixed-order computations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗