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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Search for the chiral magnetic wave using anisotropic flow of identified particles at energies available at the BNL Relativistic Heavy Ion Collider

The chiral magnetic wave (CMW) has been theorized to propagate in the deconfined nuclear medium formed in high-energy heavy-ion collisions, and to cause a difference in elliptic flow (v 2 ) between negatively and positively charged hadrons. Experimental data consistent with the CMW have been reported by the STAR Collaboration at the Relativistic Heavy Ion Collider (RHIC), based on the charge asymmetry dependence of the pion v 2 from Au+Au collisions at $\sqrt{s_{NN}}$ = 27 to 200 GeV. In this comprehensive study, we present the STAR measurements of elliptic flow and triangular flow of charged pions, along with the v 2 of charged kaons and protons, as a function of charge asymmetry in Au+Au collisions at $\sqrt{s_{NN}}$ = 27, 39, 62.4 and 200 GeV. The slope parameters extracted from the linear dependence of the v2 difference on charge asymmetry for different particle species are reported and compared in different centrality intervals. In addition, the slopes of v 2 for charged pions in small systems, i.e., p+Au and d+Au at $\sqrt{s_{NN}}$ = 200 GeV, are also presented and compared with those in large systems, i.e., Au+Au at $\sqrt{s_{NN}}$ = 200 GeV and U+U at 193 GeV. Our results provide new insights for the possible existence of the CMW, and further constrain the background contributions in heavy-ion collisions at RHIC energies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Measurement of the sodium and iodine scintillation quenching factors across multiple NaI(Tl) detectors to identify systematics

The amount of light produced by nuclear recoils in scintillating targets is strongly quenched compared to that produced by electrons. A precise understanding of the quenching factor is particularly interesting for weakly interacting massive particles (WIMP) searches and coherent elastic neutrino-nucleus scattering ( CE ν NS ) measurements since both rely on nuclear recoils, whereas energy calibrations are more readily accessible from electron recoils. There is a wide variation among the current measurements of the quenching factor in sodium iodide (NaI) crystals, especially below 10 keV, the energy region of interest for dark matter and CE ν NS studies. A better understanding of the quenching factor in NaI(Tl) is of particular interest for resolving the decades-old puzzle in the field of dark matter between the null results of most WIMP searches and the claim for dark matter detection by the DAMA/LIBRA collaboration. In this work, we measured sodium and iodine quenching factors for five small NaI(Tl) crystals grown with similar thallium concentrations and growth procedures. Unlike previous experiments, multiple crystals were tested, with measurements made in the same experimental setup to control systematic effects. The quenching factors agree in all crystals we investigated, and both sodium and iodine quenching factors are smaller than those reported by DAMA/LIBRA. The dominant systematic effect was due to the electron equivalent energy calibration originating from the nonproportional behavior of the NaI(Tl) light yield at lower energies, potentially the cause for the discrepancies among the previous measurements. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Multidifferential study of identified charged hadron distributions in Z -tagged jets in proton-proton collisions at $\sqrt{s} =13$ TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a Z boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 < p T < 100 GeV and in the pseudorapidity range 2.5 < η <4. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb –1 . Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Group-equivariant autoencoder for identifying spontaneously broken symmetries

We introduce the group-equivariant autoencoder (GE autoencoder), a deep neural network (DNN) method that locates phase boundaries by determining which symmetries of the Hamiltonian have spontaneously broken at each temperature. We use group theory to deduce which symmetries of the system remain intact in all phases, and then use this information to constrain the parameters of the GE autoencoder such that the encoder learns an order parameter invariant to these “never-broken” symmetries. This procedure produces a dramatic reduction in the number of free parameters such that the GE-autoencoder size is independent of the system size. We include symmetry regularization terms in the loss function of the GE autoencoder so that the learned order parameter is also equivariant to the remaining symmetries of the system. By examining the group representation by which the learned order parameter transforms, we are then able to extract information about the associated spontaneous symmetry breaking. We test the GE autoencoder on the 2D classical ferromagnetic and antiferromagnetic Ising models, finding that the GE autoencoder (1) accurately determines which symmetries have spontaneously broken at each temperature; (2) estimates the critical temperature in the thermodynamic limit with greater accuracy, robustness, and time efficiency than a symmetry-agnostic baseline autoencoder; and (3) detects the presence of an external symmetry-breaking magnetic field with greater sensitivity than the baseline method. Lastly, we describe various key implementation details, including a quadratic-programming-based method for extracting the critical temperature estimate from trained autoencoders and calculations of the DNN initialization and learning rate settings required for fair model comparisons.

42 ENGINEERING↗
DOE OSTI2021

Identifying the fingerprints of topological states by tuning magnetoresistance in a semimetal: The case of topological half-Heusler Pt 1 - x Au x LuSb

We report that topological materials often exhibit remarkably linear nonsaturating magnetoresistance (LMR), which is both of scientific and technological importance. However, the role of topologically nontrivial states in the emergence of such a behavior has eluded clear demonstration in experiments. Here, by reducing the coupling between the topological surface states (TSS) and the bulk carriers, we controllably tune the LMR behavior in Pt 1-x Au x LuSb into distinct plateaus in Hall resistance, which we show arise from a quantum Hall phase. This allowed us to reveal how smearing of the Landau levels, which otherwise gives rise to a quantum Hall phase, results in an LMR behavior due to strong interaction between the TSS with a positive g factor and the bulk carriers. We establish that controlling the coupling strength between the surface and the bulk carriers in topological materials can bring about dramatic changes in their magnetotransport behavior. In addition, our work outlines a strategy to reveal macroscopic physical observables of TSS in compounds with a semimetallic bulk band structure, as is the case in multifunctional Heusler compounds, thereby opening up opportunities for their utilization in hybrid quantum structures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Identifying topological superconductivity in two-dimensional transition-metal dichalcogenides

Here we study the superconducting pairing instabilities and gap functions for prototypical two-dimensional (2D) transition-metal dichalcogenides (TMDCs) WS 2 , MoTe 2 , and MoS 2 in the 2H phase under both hole and electron doping at 10 K. Our first-principles quantum many-body Green's function approach allows us to treat the full d and p manifold of orbitals with strong spin-orbit coupling, yielding pairing predictions with material-specific detail. The resulting gap functions exhibit a variety of mixed-parity superconducting states, including s, p, d, f, d ± id, and p ± ip pairing modes. In particular, we predict 3% and 4% hole-doped WS 2 to be a chiral p ± ip topological superconductor. For 1% hole-doped MoS 2 , we find a competition between three doubly degenerate chiral and nonchiral instabilities. Overall, the relative pairing strengths are found to follow the Fermi-surface topology, due to nesting between the Fermi-surface sheets. Finally, we discuss our predictions in relation to available experimental data and classify the topology of the predicted superconducting pairing symmetries.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Identifying neutron sources using recoil and time-of-flight spectroscopy

Identification of neutron sources is central to nuclear physics and its applications, from planetary science to nuclear security, yet direct source discrimination from measured neutron spectra remains fundamentally elusive. Here, we introduce a Bayesian protocol that directly infers source ensembles from measured neutron spectra by combining full-spectrum template matching with probabilistic evidence evaluation. Applying this protocol to recoil and time-of-flight spectroscopy, we recover single- and two-source configurations with strong statistical significance (beyond 4⁢𝜎) at event counts as low as ∼10 3 . These results demonstrate that neutron spectral signatures can be leveraged for robust source identification, opening a new observational window for both fundamental research and operationally driven applications.

neutron physics↗
DOE OSTI2023

Frequency Response Analysis to Monitor and Identify Changes in the Impedance of a Photovoltaic Panel Measured Online using a Power Optimizer

Photovoltaic (PV) cells are generally modeled as a current source due to photocurrent, p-n junction diodes with parasitic resistance, capacitance, and inductance. This paper proposes online frequency response analysis (FRA) to measure the impedance of a PV panel using an existing panel-level power optimizer in a PV system. The algorithm will actively perturb a small signal into a 300 W rooftop PV panel and compute its small signal impedance. The technology discussed is easy to incorporate, requires no additional hardware, doesn't alter the stability of the system, and is implemented at a steady-state point. The power optimizer initially stabilizes at an operating point and then perturbs the PV current via FRA and computes PV panel impedance. The relative standard deviation test conducted indoors under 300 W/m 2 illumination on a PV panel shows a less than 5% error rate in PV panel impedance magnitude and phase is measured using a power optimizer.

14 SOLAR ENERGY↗
DOE OSTI2022

Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods

Simulation of reservoir releases plays a critical role in social-economic functioning and our nation's security. How-ever, it is challenging to predict the reservoir release accurately because of many influential factors from natural environments and engineering controls such as the reservoir inflow and storage. Moreover, climate change and hydrological intensification causing the extreme precipitation and temperature make the accurate prediction of reservoir releases even more challenging. Machine learning (ML) methods have shown some successful applications in simulating reservoir releases. However, previous studies mainly used inflow and storage data as inputs and only considered their short-term influences (e.g, previous one or two days). In this work, we use long short-term memory (LSTM) networks for reservoir release prediction based on four input variables including inflow, storage, precipitation, and temperature and consider their long-term influences. We apply the LSTM model to 30 reservoirs in Upper Colorado River Basin, United States. We analyze the prediction performance using six statistical metrics. More importantly, we investigate the influence of the input hydrometeorological factors, as well as their temporal effects on reservoir release decisions. Results indicate that inflow and storage are the most influential factors but the inclusion of precipitation and temperature can further improve the prediction of release especially in low flows. Additionally, the inflow and storage have a relatively long-term effect on the release. These findings can help optimize the water resources management in the reservoirs.

Fan, Ming↗