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DOE OSTI2021

Bottomonium suppression and elliptic flow using Heavy Quarkonium Quantum Dynamics

We introduce a framework called Heavy Quarkonium Quantum Dynamics (HQQD) which can be used to compute the dynamical suppression of heavy quarkonia propagating in the quark-gluon plasma using real-time in-medium quantum evolution. Using HQQD we compute large sets of real-time solutions to the Schrödinger equation using a realistic in-medium complex-valued potential. We sample 2 million quarkonia wave packet trajectories and evolve them through the QGP using HQQD to obtain their survival probabilities. The computation is performed using three different HQQD model parameter sets in order to estimate our systematic uncertainty. After taking into account final state feed down we compare our results to existing experimental data for the suppression and elliptic flow of bottomonium states and find that HQQD predictions are good agreement with available data for R AA as a function of N part and p T collected at $ \sqrt{s_{\mathrm{NN}}} $ = 5 . 02 TeV. In the case of v 2 for the various states, we find that the path-length dependence of ϒ(1 s ) suppression results in quite small v 2 for ϒ(1 s ). Our prediction for the integrated elliptic flow for ϒ(1 s ) in the 10 – 90% centrality class, which now includes an estimate of the systematic error, is v 2 [ϒ(1 s )] = 0 . 003 ± 0 . 0007 ± $ {}_{0.0013}^{0.0006} $ . We also find that, due to their increased suppression, excited bottomonium states have a larger elliptic flow. Based on this observation we make predictions for v 2 [ϒ(2 s )] and v 2 [ϒ(3 s )] as a function of centrality and transverse momentum.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Neutrinoless double β decay with light sterile neutrinos: the contact terms

We study neutrinoless double-beta decay in extensions of the Standard Model that include n right-handed neutrino singlets, with masses m s below the GeV scale. Generalizing recently developed matching methods, we determine the m s dependence of the short-range nn → pp couplings that appear to leading order in the chiral effective field theory description of neutrinoless double beta decay. We focus on two scenarios, corresponding to the minimal νSM and left-right symmetric models. We illustrate the impact of our new results in the case of the νSM, showing a significant impact on the neutrinoless double-beta decay half-life when m s is in the 200–800 MeV range.

effective field theories↗

DOE OSTI2020

Measurement of quark- and gluon-like jet fractions using jet charge in PbPb and pp collisions at 5.02 TeV

The momentum-weighted sum of the electric charges of particles inside a jet, known as jet charge, is sensitive to the electric charge of the particle initiating the parton shower. This paper presents jet charge distributions in $ \sqrt{s_{\mathrm{NN}}} $ = 5.02 TeV lead-lead (PbPb) and proton-proton (pp) collisions recorded with the CMS detector at the LHC. These data correspond to integrated luminosities of 404 μb$^{−1}$ and 27.4 pb$^{−1}$ for PbPb and pp collisions, respectively. Leveraging the sensitivity of the jet charge to fundamental differences in the electric charges of quarks and gluons, the jet charge distributions from simulated events are used as templates to extract the quark- and gluon-like jet fractions from data. The modification of these jet fractions is examined by comparing pp and PbPb data as a function of the overlap of the colliding Pb nuclei (centrality). This measurement tests the color charge dependence of jet energy loss due to interactions with the quark-gluon plasma. No significant modification between different centrality classes and with respect to pp results is observed in the extracted quark- and gluon-like jet fractions.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Production of $ {\textrm{K}}_{\textrm{S}}^0 $, Λ ($ \overline{\Lambda}$), Ξ ± , and Ω ± in jets and in the underlying event in pp and p–Pb collisions

The production of strange hadrons ($K^{0}_{S}$, Λ, Ξ ± , and Ω ± ), baryon-to-meson ratios (Λ/$K^{0}_{S}$, Ξ/$K^{0}_{S}$, and Ω/$K^{0}_{S}$), and baryon-to-baryon ratios (Ξ/Λ, Ω/Λ, and Ω/Ξ) associated with jets and the underlying event were measured as a function of transverse momentum (p T ) in pp collisions at $\sqrt{s}$ = 13 TeV and p Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV with the ALICE detector at the LHC. The inclusive production of the same particle species and the corresponding ratios are also reported. The production of multi-strange hadrons, Ξ ± and Ω ± , and their associated particle ratios in jets and in the underlying event are measured for the first time. In both pp and p–Pb collisions, the baryon-to-meson and baryon-to-baryon yield ratios measured in jets differ from the inclusive particle production for low and intermediate hadron p T (0.6–6 GeV/c). Ratios measured in the underlying event are in turn similar to those measured for inclusive particle production. In pp collisions, the particle production in jets is compared with P YTHIA 8 predictions with three colour-reconnection implementation modes. None of them fully reproduces the data in the measured hadron p T region. The maximum deviation is observed for Ξ ± and Ω ± which reaches a factor of about six. The event multiplicity dependence is further investigated in p–Pb collisions. In contrast to what is observed in the underlying event, there is no significant event-multiplicity dependence for particle production in jets. The presented measurements provide novel constraints on hadronisation and its Monte Carlo description. In particular, they demonstrate that the fragmentation of jets alone is insufficient to describe the strange and multi-strange particle production in hadronic collisions at LHC energies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Study of very forward energy and its correlation with particle production at midrapidity in pp and p-Pb collisions at the LHC

The energy deposited at very forward rapidities (very forward energy) is a powerful tool for characterising proton fragmentation in pp and p-Pb collisions. The correlation of very forward energy with particle production at midrapidity provides direct insights into the initial stages and the subsequent evolution of the collision. Furthermore, the correlation with the production of particles with large transverse momenta at midrapidity provides information complementary to the measurements of the underlying event, which are usually interpreted in the framework of models implementing centrality-dependent multiple parton interactions. Results about very forward energy, measured by the ALICE zero degree calorimeters (ZDCs), and its dependence on the activity measured at midrapidity in pp collisions at $\sqrt{s}$ = 13 TeV and in p-Pb collisions at $\sqrt{s{NN}}$ = 8.16 TeV are discussed. The measurements performed in pp collisions are compared with the expectations of three hadronic interaction event generators: PYTHIA 6 (Perugia 2011 tune), PYTHIA 8 (Monash tune), and EPOS LHC. These results provide new constraints on the validity of models in describing the beam remnants at very forward rapidities, where perturbative QCD cannot be used.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Inclusive and multiplicity dependent production of electrons from heavy-flavour hadron decays in pp and p-Pb collisions

Measurements of the production of electrons from heavy-flavour hadron decays in pp collisions at $\sqrt{s}$ = 13 TeV at midrapidity with the ALICE detector are presented down to a transverse momentum (p T ) of 0.2 GeV/c and up to p T = 35 GeV/c, which is the largest momentum range probed for inclusive electron measurements in ALICE. In p-Pb collisions, the production cross section and the nuclear modification factor of electrons from heavy-flavour hadron decays are measured in the p T range 0.5 < p T < 26 GeV/c at $\sqrt{s_{NN}}$= 8.16 TeV. The nuclear modification factor is found to be consistent with unity within the statistical and systematic uncertainties. In both collision systems, first measurements of the yields of electrons from heavy-flavour hadron decays in different multiplicity intervals normalised to the multiplicity-integrated yield (self-normalised yield) at midrapidity are reported as a function of the self-normalised charged-particle multiplicity estimated at midrapidity. The self-normalised yields in pp and p-Pb collisions grow faster than linear with the self-normalised multiplicity. A strong p T dependence is observed in pp collisions, where the yield of high-p T electrons increases faster as a function of multiplicity than the one of low-p T electrons. The measurement in p-Pb collisions shows no p T dependence within uncertainties. The self-normalised yields in pp and p-Pb collisions are compared with measurements of other heavy-flavour, light-flavour, and strange particles, and with Monte Carlo simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Measurement of $J/ψ$ production cross-sections in $pp$ collisions at $\sqrt{s}$ = 5 TeV

The production cross-sections of J/ψ mesons in proton-proton collisions at a centre-of-mass energy of $\sqrt{s} = 5 TeV are measured using a data sample corresponding to an integrated luminosity of 9.13 ± 0.18 pb -1 , collected by the LHCb experiment. The cross-sections are measured differentially as a function of transverse momentum, p T , and rapidity, y, and separately for J/ψ mesons produced promptly and from beauty hadron decays (nonprompt). With the assumption of unpolarised J/ψ mesons, the production cross-sections integrated over the kinematic range 0 < p T < 20 GeV/c and 2.0 < y < 4.5 are σ prompt J/Ψ = 8.154 ± 0.010 ± 0.283 μb, σnonprompt J/Ψ = 0.820 ± 0.003 ± 0.034 μb, where the first uncertainties are statistical and the second systematic. These cross-sections are compared with those at $\sqrt{s}$ = 8 TeV and 13 TeV, and are used to update the measurement of the nuclear modification factor in proton-lead collisions for J/ψ mesons at a centre-of-mass energy per nucleon pair of $\sqrt{s_{NN}}$ = 5 TeV. The results are compared with theoretical predictions.

quarkonium↗

DOE OSTI2023

Probing the chiral magnetic wave with charge-dependent flow measurements in Pb-Pb collisions at the LHC

The Chiral Magnetic Wave (CMW) phenomenon is essential to provide insights into the strong interaction in QCD, the properties of the quark-gluon plasma, and the topological characteristics of the early universe, offering a deeper understanding of fundamental physics in high-energy collisions. Measurements of the charge-dependent anisotropic flow coefficients are studied in Pb-Pb collisions at center-of-mass energy per nucleon-nucleon collision $\sqrt{s_{\textrm{NN}}}$ = 5.02 TeV to probe the CMW. In particular, the slope of the normalized difference in elliptic (v 2 ) and triangular (v 3 ) flow coefficients of positively and negatively charged particles as a function of their event-wise normalized number difference, is reported for inclusive and identified particles. The slope ${r}_3^{\textrm{Norm}}$ is found to be larger than zero and to have a magnitude similar to ${r}_2^{\textrm{Norm}}$, thus pointing to a large background contribution for these measurements. Furthermore, ${r}_2^{\textrm{Norm}}$ can be described by a blast wave model calculation that incorporates local charge conservation. In addition, using the event shape engineering technique yields a fraction of CMW (f CMW ) contribution to this measurement which is compatible with zero. This measurement provides the very first upper limit for f CMW , and in the 10–60% centrality interval it is found to be 26% (38%) at 95% (99.7%) confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Abstract Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray computed tomography (CT) enables volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) have been shown to excel at segmentation tasks; however, memory constraints, class imbalance, and lack of sufficient training data often prohibit their deployment in high resolution volumetric domains. Our 3D convolutional NN implementation mitigates these challenges and accurately segments full resolution CT scans of thermal sprayed materials with maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

Martinez, Carianne↗

DOE OSTI2022

Viscoelastic-Mapping of Cellulose Nanofibrils Using Low-Total-Force Contact Resonance Force Microscopy (LTF-CRFM)

Low-total-force contact resonance force microscopy (LTF-CRFM), an atomic force microscopy method, is introduced as a non-destructive means to quantify the local viscoelastic loss tangent (tan..delta..) of supported cellulose nanofibrils (CNFs). The method limits static and dynamic forces during measurement to minimize substrate and geometry effects and to reduce the potential for stress-induced CNF damage. LTF-CRFM uses Brownian motion to achieve the thermally-limited lowest dynamic force, while approaching adhesive pull-off to achieve the low static force. LTF-CRFM measurements were shown to generate analyzable data without evidence of nonlinear artifacts and without damage to the CNF over static forces ranging from 11.6 to 84.6 nN. The measured tan..delta.. of CNFs was 0.015 +/- 0.0094, which is the first reported tan..delta.. measurement of an isolated CNF. Finally, LTF-CRFM successfully mapped tan..delta.. along the length of CNFs to determine that kink defects along the CNF do not impart a local viscoelastic property change at the spatial resolution of the measurement.

BIOMASS FUELS↗

DOE OSTI2022

A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

Here, we present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to predict ternary systems as a function of composition and temperature. We have chosen the energetics of the FCC solid solution phase in 226 binaries consisting of 23 elements at 11 different temperatures to demonstrate the feasibility. The number of binary data points included in the present study is 102,000. We select six ternaries to augment the binary dataset to investigate their influence on the NN prediction accuracy. We examine the sensitivity of data sampling on the prediction accuracy of NNs over selected ternary systems. It is anticipated that the current DL workflow can be further elevated by integrating advanced descriptors beyond the elemental composition and more curated training datasets to improve prediction accuracy and applicability.

42 ENGINEERING↗

DOE OSTI2026

Reducing systematic bias in machine learning applications to J/ψ signal extraction in high-energy nuclear physics

Machine learning techniques are increasingly used in high-energy nuclear physics because they can exploit multivariate correlations more efficiently than conventional cut-based analyses. A central challenge is the construction of training samples that faithfully reproduce the detector response observed in data. Signal samples are usually derived from detector simulations; therefore, mismatches between simulation and data can degrade classifier performance and introduce systematic biases. This work presents two practical correction procedures, namely cumulative distribution function (CDF) mapping and a shift-and-scale transformation, to align simulated signal features with those measured in data. Their performance is demonstrated with $J$/$\psi$ yield measurements in $\sqrt{s_{nn}}$ = 200 GeV Ru+Ru and Zr+Zr collisions recorded by STAR. A set of self-consistency tests shows that these procedures substantially suppress the systematic bias associated with data-simulation discrepancies in machine-learning-based signal extraction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

A machine learning approach to fracture mechanics problems

Analytical and empirical solutions to engineering problems are usually preferred because of their convenience in applications. However, they are not always accessible in complex problems. A new class of solutions, based on machine learning (ML) models such as regression trees and neural networks (NNs), are proposed and their feasibility and value are demonstrated through the analysis of fracture toughness measurements. It is found that both solutions based on regression trees and NNs can provide accurate results for the specific problem, but NN-based solutions outperform regression-tree-based solutions in terms of their simplicity. Here this example demonstrates that ML solutions are a major improvement over analytical and empirical solutions in terms of both reliable functionality and rapid deployment. When analytical solutions are not available, the use of ML solutions can overcome the limitations of empirical solutions and substantially change the way that engineering problems are solved.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Data coverage assessment on neural network based digital twins for autonomous control system

We report in a recently developed Nearly Autonomous Management and Control (NAMAC) system, neural networks (NNs) are used to develop digital twins for diagnosis (DT-Ds). However, NNs are not usually considered extrapolation models and may result in large errors if they are applied to unseen data outside the training data (uncovered). In this study, we propose a data coverage assessment (DCA) to determine if the NN-based DT-Ds are extrapolated based on their epistemic uncertainty. The uncertainty quantification algorithms and uncertainty thresholds are selected based on the confusion matrix of classifying evaluation data into covered or uncovered data. To demonstrate the adaptability of the proposed framework, we applied it to a basic feedforward neural network and a more advanced recurrent neural network based on a more nonlinear database. Case studies show that the proposed framework can distinguish unseen data for both basic and advanced applications with proper uncertainty quantification algorithms and thresholds.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

A parametric approach to identify synergistic domains of process intensification for reactive separation

Process intensification aims to combine multiple tasks within multi-functional units to drastically improve economic, energy or sustainability metrics of a chemical process. Limited work exists to systematically identify the synergistic domains where intensification outperforms its nonintensified counterpart. In this work, we computationally derive the synergistic domains of a reactive separation system. Specifically, we first postulate general models for both intensified and nonintensified systems. We use these models to generate data to train a ReLU-type artifical neural network (ANN). Further, the trained ReLU-NN model is formulated as a multi-parametric mixed-integer linear program (mp-MILP), and the critical regions of this mp-MILP define the synergistic feasible domains of intensification. We have derived these synergistic domains of vapor–liquid equilibrium (VLE)-based reactive separation for several industrial applications. These synergistic domains enable quick screening of properties that favor intensification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Surrogate construction via weight parameterization of residual neural networks

Surrogate model development is a critical step for uncertainty quantification or other sample-intensive tasks for complex computational models. Here, in this work, we develop a multi-output surrogate form using a class of neural networks (NNs) that employ shortcut connections, namely Residual NNs (ResNets). ResNets are known to regularize the surrogate learning problem and improve the efficiency and accuracy of the resulting surrogate. Inspired by the continuous, Neural ODE analogy, we augment ResNets with weight parameterization strategy with respect to ResNet depth. Weight-parameterized ResNets regularize the NN surrogate learning problem and allow better generalization with a drastically reduced number of learnable parameters. We demonstrate that weight-parameterized ResNets are more accurate and efficient than conventional feed-forward multi-layer perceptron networks. We also compare various options for parameterization of the weights as functions of ResNet depth. We demonstrate the results on both synthetic examples and a large scale earth system model of interest.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Ensemble cure kinetics network (ECK-Net): A method to derive cure kinetics of thermosetting resin

This paper introduces an Ensemble Cure Kinetics Network (ECK-Net), a neural network (NN)–based framework for modeling the cure kinetics of thermosetting resins within a phenomenological context. ECK-Net replaces traditional analytic models, which require extensive chemical insight and multiple isothermal/non-isothermal experiments, with a data-driven surrogate that maps nonlinear relationships between temperature, degree of cure, and reaction rate from differential scanning calorimetry data. The proposed approach predicts input-dependent kinetic coefficients of a generalized nth-order reaction equation rather than reaction rates directly, enabling a single unified model to represent various epoxy systems without relying on iso-conversional analysis or predefined functional forms. To ensure robustness, multiple independently trained networks under different random initializations are blended through an ensemble strategy, effectively mitigating the stochastic variability inherent to neural networks. The framework is validated using experimental datasets from multiple resin systems, including aerospace-grade materials (Toray 3900-2, Cycom 5320-1, and Hexcel 8552) and a windmill-grade resin (RIMR 035c). The model accurately reproduces the temporal evolution of the degree of cure under manufacturers’ recommended cure cycles across all tested resins systems, yielding Pearson’s correlation coefficients of 0.992, 0.994, 0.993, 0.997, respectively. To demonstrate process-level applicability, the trained network was implemented within the Abaqus environment to simulate out-of-autoclave (OOA) curing process of the CFRP panel composed of Toray T830H-6K/3900-2D prepreg. The simulation results showed excellent agreement with experimental temperature response (maximum peak temperature, simulation: 189.6 °C, experiment: 188.5 °C) and the final degree of cure (simulation: 0.948, experiment: 0.960 ± 0.013), confirming ECK-Net’s capability as a reliable alternative to conventional cure kinetics modeling methods.

Composite curing↗

DOE OSTI2021

Azo–hydrazone tautomerism in organometallic complexes triggered by a -Re(CO) 3 (L) core: A spectroscopic and theoretical study

The spectroscopic properties and tautomeric behavior of five novel Rhenium (I) tricarbonyl complexes bearing an azo ligand are presented. The organic ligand is stable in solution as the hydrazone tautomer. It remains as hydrazone in different medium conditions (solvent, concentration, pH, etc.) without the formation of detectable amounts of the azo tautomer. However, the complexation of this ligand to the strong electron-withdrawing fragment -Re(CO) 3 X (X = Cl – , Br – ) causes tautomerism to appear in the organic moiety. Two well-defined regions in the electronic spectra for both tautomers were observed, allowing the azo/enol-keto/hydrazone equilibrium to be followed. TD-DFT calculations indicate that for the keto/hydrazone form, the main absorption band is attributed to an IL transition. In contrast, the azo/enol species shows a major contribution of the 1 MLCT (dπ(Re) → dπ*(NN)) transition. The tautomeric equilibrium is easily shifted using solvents with different dielectric constant and hydrogen bond donor/acceptor (HBD/HBA) abilities. Also, the tautomerization process is deeply influenced by the electronic properties of the axial ligand. Therefore, this behavior represents a different strategy for the design of novel materials with optical properties. Furthermore, tautomerism is affected by the concentration of complexes and the presence of water in solvents. Finally, the acid-base behavior of these compounds in a 50% v/v ethanol-buffer system showed that the azo/enol tautomer is stabilized in acidic media. At the same time, an increase of pH promotes tautomerization toward the keto/hydrazone, followed by forming the anionic form at pH > 8.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗