Integrated photonuclear cross sections in the giant dipole resonance of odd-mass actinide nuclei

Abstract

This study explores the integrated total photonuclear cross section (σ0) within the context of the giant dipole resonance (GDR) in odd-mass actinide nuclei. Using artificial neural networks (ANNs) and adaptive neuro-fuzzy ınference system (ANFIS) machine learning algorithms, we analyze the GDR behaviors associated with the σ0 values in these nuclei. The modeling results obtained from ANFIS and ANN are compared among themselves and with the Translational Galilean Invariant Quasiparticle Phonon Nuclear Model (TGI-QPNM) and experimental data. Machine learning analysis and TGI-QPNM results provide valuable insights into the GDR characteristics of odd-mass actinides, shedding light on their photonuclear properties. The ANFIS model has achieved an R2 value of 0.98 and an RMSE of 0.19, while the ANN model (LM) has yielded an R2 value of 0.95 and an RMSE of 0.24. These findings deepen our understanding of nuclear physics, underscoring the role of artificial intelligence techniques in deciphering complex phenomena within nuclear structures. In conclusion, our study suggests that the ANFIS model, in agreement with TGI-QPNM results, generally outperforms the ANN (LM) method and could be a more effective tool for estimating the energy-weighted sum rule for GDR.