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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 OSTI2024

Determination of short- and long-distance contributions in B 0 → K * 0 μ + μ − decays

An amplitude analysis of the B 0 → K * 0 μ + μ − decay is presented. The analysis is based on data collected by the LHCb experiment from proton-proton collisions at s = 7 , 8 and 13 TeV, corresponding to an integrated luminosity of 4.7 fb − 1 . For the first time, Wilson coefficients and nonlocal hadronic contributions are accessed directly from the unbinned data, where the latter are parametrized as a function of q 2 with a polynomial expansion. Wilson coefficients and nonlocal hadronic parameters are determined under two alternative hypotheses: the first relies on experimental information alone, while the second one includes information from theoretical predictions for the nonlocal contributions. Both models obtain similar results for the parameters of interest. The overall level of compatibility with the Standard Model is evaluated to be between 1.8 and 1.9 standard deviations when looking at the C 9 Wilson coefficient alone, and between 1.3 and 1.4 standard deviations when considering the full set of C 9 , C 10 , C 9 ′ and C 10 ′ Wilson coefficients. The ranges reflect the theoretical assumptions made in the analysis. © 2024 CERN, for the LHCb Collaboration 2024 CERN

Aaij, R. (ORCID:0000000305331952)↗
DOE OSTI2022

Benchmarking Quantum Processor Performance through Quantum Distance Metrics Over An Algorithm Suite

Quantum computing is poised to solve computational paradigms that classical computing could never feasibly reach. Tasks such as prime factorization to Quantum Chemistry are examples of classically difficult problems that have analogous algorithms that are sped up on quantum computers. To attain this computational advantage, we must first traverse the noisy intermediate scale quantum (NISQ) era, in which quantum processors suffer from compounding noise factors that can lead to unreliable algorithm induction producing noisy results. We describe QASMBench, a suite of QASM-level (Quantum assembly language) benchmarks that challenge all realisable angles of quantum processor noise. We evaluate a large portion of these algorithms by performing density matrix tomography on 14 IBMQ Quantum devices.

Stein, Samuel A.↗
DOE OSTI2023

Fungal organic acid uptake of mineral-derived K is dependent on distance from carbon hotspot

Fungal mineral weathering regulates the bioavailability of inorganic nutrients from mineral surfaces to organic matter and increase the bioavailable fraction of nutrients. Such weathering strategies are classified as biomechanical or biochemical. In the case of fungal uptake of mineral nutrients through biochemical weathering, it is widely hypothesized that uptake of inorganic nutrients occurs through organic acid chelation, but such processes have not been directly visualized. This is in part due to challenges in probing the complex and heterogeneous soil environment. Here, using an epoxy-based, mineral-doped soil micromodel platform, which emulates soil mineralogy and porosity, we visualize the molecular mechanisms of mineral weathering. Mass spectrometry imaging revealed differences in the distribution of fungal exudates, citric acid, and tartaric acid on the soil micromodels in presence of minerals. Citric acid was detected closer to the nutrient-rich inoculation point, whereas tartaric acid was highly abundant away from inoculation point. This suggested that the organic acid exuded by the fungi depended on the proximity from the carbon-rich organic substrate at the point of inoculation. Using a combination of X-ray fluorescence and X-ray near edge structure analysis, we identified citric acid- and tartaric acid-bound K within fungal hyphae networks grown in the presence of minerals. Combined, our results provide direct evidence that fungi uptake and transport mineral derived nutrient organic acid chelation. The results of this study provided unprecedented visualization of fungal uptake and transport of K + , while resolving the indirect weathering mechanism of fungal K uptake from mineral interfaces.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Measurements of angular distance and momentum ratio distributions in three-jet and $${\text {Z}}$$ + two-jet final states in $${\text {p}}{\text {p}}$$ collisions

Collinear (small-angle) and large-angle, as well as soft and hard radiations are investigated in three-jet and Z + two-jet events collected in proton-proton collisions at the LHC. The normalized production cross sections are measured as a function of the ratio of transverse momenta of two jets and their angular separation. The measurements in the three-jet and Z + two-jet events are based on data collected at a center-of-mass energy of 8TeV, corresponding to an integrated luminosity of 19.8fb -1 . The Z + two-jet events are reconstructed in the dimuon decay channel of the Z boson. The three-jet measurement is extended to include $\sqrt {s}$=13TeV data corresponding to an integrated luminosity of 2.3fb -1 . The results are compared to predictions from event generators that include parton showers, multiple parton interactions, and hadronization. The collinear and soft regions are in general well described by parton showers, whereas the regions of large angular separation are often best described by calculations using higher-order matrix elements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah

Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ~5 db, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ~0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ~2-5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ~15 db.

58 GEOSCIENCES↗