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Träfflista för sökning "WFRF:(Merchan E.) srt2:(2020-2021)"

Search: WFRF:(Merchan E.) > (2020-2021)

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1.
  • Glasbey, JC, et al. (author)
  • 2021
  • swepub:Mat__t
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2.
  • 2021
  • swepub:Mat__t
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4.
  • Avigo, R., et al. (author)
  • Low-lying electric dipole gamma-continuum for the unstable Fe-62,64 nuclei : Strength evolution with neutron number
  • 2020
  • In: Physics Letters B. - : ELSEVIER. - 0370-2693 .- 1873-2445. ; 811
  • Journal article (peer-reviewed)abstract
    • The gamma-ray emission from the nuclei Fe-62,Fe-64 following Coulomb excitation at bombarding energy of 400-440 AMeV was measured with special focus on E1 transitions in the energy region 4-8 MeV. The unstable neutron-rich nuclei Fe-62,Fe-64 were produced at the FAIR-GSI laboratories and selected with the FRS spectrometer. The gamma decay was detected with AGATA. From the measured gamma-ray spectra the summed E1 strength is extracted and compared to microscopic quasi-particle phonon model calculations. The trend of the E1 strength with increasing neutron number is found to be fairly well reproduced with calculations that assume a rather complex structure of the 1(-) states (three-phonon states) inducing a strong fragmentation of the E1 nuclear response below the neutron binding energy.
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5.
  • Balázs, C., et al. (author)
  • A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
  • 2021
  • In: Journal of High Energy Physics (JHEP). - : Springer Nature. - 1126-6708 .- 1029-8479. ; 2021:5
  • Journal article (peer-reviewed)abstract
    • Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms.
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  • Result 1-5 of 5

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