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Nuclear Theory·nucl-th

Monday·November 7, 2022

7 papers—4 primary·3 cross-listed

All7Primary4Cross-lists3
  1. 05

    [Submitted on 3 Nov 2022] (cross-list from quant-ph)

    Methods on compositeness and related aspects

    J.A. Oller🇪🇸

    In many physical applications, bound states and/or resonances are observed, which raises the question whether these states are elementary or composite. Here we elaborate on several methods for calculating the compositeness of bound states and resonances in Quantum Mechanics, and in Quantum Field Theory by introducing particle number operators. For resonances is typically complex and we discuss how to get meaningful results by using certain phase transformations in the matrix.

    Comments:
    8 pages, 2 figures, contributed to "XVth Quark Confinement and the Hadron Spectrum Conference" (1-6 August 2022, Stavanger, Norway)
    Subjects:
    Quantum Physics (quant-ph); High Energy Physics — Phenomenology (hep-ph); Nuclear Theory (nucl-th)
    arXiv:
    2211.02083 [pdf]
    EPJ Web Conf.(2022)·2 citations
  2. 06

    [Submitted on 3 Nov 2022] (cross-list from hep-ph)

    Compositeness and several applications to exotic hadronic states with heavy quarks

    J.A. Oller🇪🇸 · Z.-H. Guo🇨🇳

    Several methods for studying the nature of a resonance are applied to resonances recently discovered in the bottonomium and charmonium sectors. We employ the effective-range expansion, the saturation of the width and compositeness of a resonance, as well as direct fits to data. The latter stem from generic -matrix parameterization that account for relevant dynamical features associated to channels that couple strongly in an energy region around the resonance masses, in which their thresholds also lie. We report on results obtained with these methods for the resonances , , , , , , , and .

    Comments:
    8 pages, 2 figures, contributed to "XVth Quark Confinement and the Hadron Spectrum Conference" (1-6 August 2022, Stavanger, Norway)
    Subjects:
    High Energy Physics — Phenomenology (hep-ph); Nuclear Theory (nucl-th)
    arXiv:
    2211.02090 [pdf]
    EPJ Web Conf.(2022)·0 citations
  3. 07

    [Submitted on 4 Nov 2022] (cross-list from stat.CO)

    Multi-output Gaussian processes for inverse uncertainty quantification in neutron noise analysis

    Paul Lartaud🇫🇷 · Philippe Humbert🇫🇷 · Josselin Garnier🇫🇷

    In a fissile material, the inherent multiplicity of neutrons born through induced fissions leads to correlations in their detection statistics. The correlations between neutrons can be used to trace back some characteristics of the fissile material. This technique known as neutron noise analysis has applications in nuclear safeguards or waste identification. It provides a non-destructive examination method for an unknown fissile material. This is an example of an inverse problem where the cause is inferred from observations of the consequences. However, neutron correlation measurements are often noisy because of the stochastic nature of the underlying processes. This makes the resolution of the inverse problem more complex since the measurements are strongly dependent on the material characteristics. A minor change in the material properties can lead to very different outputs. Such an inverse problem is said to be ill-posed. For an ill-posed inverse problem the inverse uncertainty quantification is crucial. Indeed, seemingly low noise in the data can lead to strong uncertainties in the estimation of the material properties. Moreover, the analytical framework commonly used to describe neutron correlations relies on strong physical assumptions and is thus inherently biased. This paper addresses dual goals. Firstly, surrogate models are used to improve neutron correlations predictions and quantify the errors on those predictions. Then, the inverse uncertainty quantification is performed to include the impact of measurement error alongside the residual model bias.

    Comments:
    23 pages, 7 figures, 6 tables
    Subjects:
    stat.CO (stat.CO); Nuclear Theory (nucl-th); stat.AP (stat.AP); Machine Learning (stat.ML)
    arXiv:
    2211.02465 [pdf]
    Nucl.Sci.Eng.(2023)·1 citation

Affiliations

first authorsco-authorsvia INSPIRE