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Nuclear Theory·nucl-th

Friday·April 7, 2023

6 papers—3 primary·3 cross-listed

All6Primary3Cross-lists3
  1. 01

    Asymptotic normalization coefficients of alpha-particle removal from O()

    L. D. Blokhintsev · A. S. Kadyrov · A. M. Mukhamedzhanov · D. A. Savin

    Asymptotic normalization coefficients (ANC) determine the overall normalization of cross sections of peripheral radiative capture reactions. In a recent paper [Blokhintsev et al., Eur. Phys. J. A 58, 257 (2022)], we considered the ANC for the virtual decay O MeV)C(g.s.). In the present paper, which can be regarded as a continuation of the previous, we treat the ANCs for the vertices OC(g.s.) corresponding to the other three bound excited states of O (, , , ). ANCs () are found by analytic continuation in energy of the C -wave partial scattering amplitudes, known from the phase-shift analysis of experimental data, to the pole corresponding to the O bound state and lying in the unphysical region of negative energies. To determine , the scattering data are approximated by the sum of polynomials in energy in the physical region and then extrapolated to the pole. For a more reliable determination of the ANCs, various forms of functions expressed in terms of phase shifts were used in analytical approximation and subsequent extrapolation.

    nucl-thastro-ph.SRnucl-ex0 citations
  2. 02

    Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations

    Gurtej Kanwar🇨🇭 · Alessandro Lovato🇺🇸 · Noemi Rocco🇺🇸 · Michael Wagman🇺🇸

    The Green's function Monte Carlo (GFMC) method provides accurate solutions to the nuclear many-body problem and predicts properties of light nuclei starting from realistic two- and three-body interactions. Controlling the GFMC fermion-sign problem is crucial, as the signal-to-noise ratio decreases exponentially with Euclidean time, requiring significant computing resources. Inspired by similar scenarios in lattice quantum field theory and spin systems, in this work, we employ integration contour deformations to improve the GFMC signal-to-noise ratio. Machine learning techniques are used to select optimal contours with minimal variance from parameterized families of deformations. As a proof of principle, we consider the deuteron binding energies and Euclidean density response functions. We only observe mild signal-to-noise improvement for the binding energy case. On the other hand, we achieve an order of magnitude reduction of the variance for Euclidean density response functions, paving the way for computing electron- and neutrino-nucleus cross-sections of larger nuclei.

    nucl-thhep-lathep-phPRC(2024)·8 citations
  3. 03

    Glasma properties in small proper time expansion

    Margaret E. Carrington🇨🇦 · Wade N. Cowie🇨🇦 · Bryce T. Friesen🇨🇦 · Stanislaw Mrowczynski🇵🇱 · Doug Pickering🇨🇦

    In a series of works by two of us, various characteristics of the glasma from the earliest phase of relativistic heavy-ion collisions have been studied using a proper time expansion. These characteristics include: energy density, longitudinal and transverse pressures, collective flow, angular momentum and parameters of jet quenching. In this paper we extend the proper time interval where our results are reliable by working at higher order in the expansion. We also generalize our previous study of jet quenching by extending our calculations to consider inhomogeneous glasma. Inhomogeneities are an important aspect of physically realistic systems that are difficult to include in calculations and are frequently ignored.

    nucl-thhep-phPRC(2023)·9 citations

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first authorsco-authorsvia INSPIRE