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Estimations of level density parameters by using artificial neural network for phenomenological level density models.

Hasan Özdoğan1, Yiğit Ali Üncü2, Mert Şekerci3

  • 1Antalya Bilim University, Vocational School of Health Services, Department of Medical Imaging Techniques, 07190, Antalya, Turkey.

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PubMed
Summary

This study introduces a Bayesian-based algorithm using artificial neural networks (ANNs) to accurately estimate nuclear level density parameters for phenomenological models. The developed method shows high accuracy, improving nuclear reaction and data evaluations.

Keywords:
Artificial neural networkBayesian-based algorithmLevel density modelsLevel density parameters

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Area of Science:

  • Nuclear Physics
  • Computational Physics
  • Data Science

Background:

  • Accurate estimation of nuclear level density parameters is crucial for nuclear reaction modeling and data evaluation.
  • Phenomenological models like Gilbert Cameron Model (GCM), Back Shifted Fermi Gas Model (BSFGM), and Generalised Super Fluid Model (GSM) are widely used but require precise parameterization.
  • Existing methods for parameter estimation can be computationally intensive or lack sufficient accuracy.

Purpose of the Study:

  • To develop and present an efficient Bayesian-based algorithm utilizing artificial neural networks (ANNs) for estimating nuclear level density parameters.
  • To apply the developed algorithm to GCM, BSFGM, and GSM, comparing the obtained parameters with the Reference Input Parameter Library (RIPL).
  • To validate the accuracy of the new parameters by recalculating photo-neutron cross-sections for specific tin isotopes.

Main Methods:

  • Development of artificial neural network (ANN) algorithms for parameter estimation.
  • Implementation of an efficient Bayesian-based algorithm to predict unknown model parameters from observed data.
  • Application of the Bayesian method to estimate parameters for GCM, BSFGM, and GSM.
  • Comparison of estimated parameters with RIPL data and validation through photo-neutron cross-section calculations using TALYS 1.95.

Main Results:

  • The Bayesian method achieved high correlation coefficients (R values) of 0.9946 for BSFGM, 0.9981 for GCM, and 0.9824 for GSM.
  • The newly obtained level density parameters were used to update the default parameters in the TALYS 1.95 code.
  • Photo-neutron cross-section calculations for 117Sn, 118Sn, 119Sn, and 120Sn isotopes showed improved accuracy when using the refined parameters.

Conclusions:

  • The developed Bayesian-based ANN algorithm provides accurate estimations for nuclear level density parameters.
  • The refined parameters enhance the predictive power of nuclear reaction models, particularly for photo-neutron cross-section calculations.
  • This approach offers a valuable tool for nuclear data evaluations and theoretical physics research.