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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Determination of asymptotic normalization coefficients for the channel 16 O→ α+ 12 C: excited state 16 O(0 + ; 6.05 MeV)

Asymptotic normalization coefficients (ANC) determine the overall normalization of cross sections of peripheral radiative capture reactions. In the present paper, we treat the ANC C for the virtual decay 16 O(0 + ; 6.05 MeV)→ α+ 12 C(g.s.), the known values of which are characterized by a large spread (0.29 - 1.65) × 10 3 fm -1/2 . The ANC C is found by analytic continuation in energy of the α 12 C s-wave scattering amplitude, known from the phase-shift analysis of experimental data, to the pole corresponding to the 16O bound state and lying in the unphysical region of negative energies. To determine C, two different methods of analytic continuation are used. In the first method, the scattering data are approximated by the sum of polynomials in energy in the physical region and then extrapolated to the pole. The best way of extrapolation is chosen on the basis of the exactly solvable model. Within the second approach, the ANC C is found by solving the Schrödinger equation for the two-body α 12 C potential, the parameters of which are selected from the requirement of the best description of the phase-shift analysis data at a fixed experimental binding energy of 16 O(0 + ; 6.05 MeV) in the α+ 12 C channel. The values of the ANC C obtained within these two methods lie in the interval (886–1139) fm -1/2 .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Lifetimes of excited states in $^{16}$C as a benchmark for ab initio developments

Lifetimes of higher-lying states ($2_2^+$ and $4_1^+$) in 16 C have been measured, employing the Gammasphere and Microball detector arrays, as key observables to test and refine ab initio calculations based on interactions developed within chiral Effective Field Theory. The presented experimental constraints to these lifetimes of $\tau ({2_2^+}) = [244, 446]\,~\textrm{fs}$ and $\tau ({4_1^+}) = [1.8, 4]\,~\textrm{ps}$, combined with previous results on the lifetime of the $2_1^+$ state of 16 C, provide a rather complete set of key observables to benchmark the theoretical developments. We present No-Core Shell-Model calculations using state-of-the-art chiral 2- (NN) and 3-nucleon (3N) interactions at next-to-next-to-next-to-leading order for both the NN and the 3N contributions and a generalized natural-orbital basis (instead of the conventional harmonic-oscillator single-particle basis) which reproduce, for the first time, the experimental findings remarkably well. The level of agreement of the new calculations as compared to the CD-Bonn meson-exchange NN interaction is notable and presents a critical benchmark for theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Probing ultrafast excited-state dynamics using EUV-IR six-wave-mixing emission spectroscopy

Non-linear processes such as four-wave-mixing have become instrumental in attosecond EUV spectroscopy. Using EUV high harmonics in conjunction with collinear near-infrared and mid-infrared fields, we extended this approach to high-order-mixing between three colors. Specifically, we find that atomic resonances in neon exhibit a significant cross section for six-wave mixing. The MIR probe frequency tunability in our multicolor scheme is employed to access several optically dark resonances and probe the quantum beat of a coherent electronic wavepacket using background-free EUV emission as a diagnostic. This technique can be easily extended to other atomic and molecular systems, and opens the door to multi-dimensional non-linear spectroscopy.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Machine-Learning for Excited-State Dynamics

The primary objective of this computational chemistry sciences team is to design a machine learning NAMD environment that will utilize current petascale and future exascale computational capabilities to advance understanding of charge and energy flow in materials. Our machine-learning NAMD environment will 1) integrate advanced NAMD capabilities directly into electronic structure software (e.g., ABINIT, Quantum Espresso, VASP, etc.); 2) merge the preparatory tools of Pychemia into PYXAID and Avogadro environments so that massive data collection from NAMD simulations.

36 MATERIALS SCIENCE↗