Prediction of gamma ray spectrum for 22Na source by feed forward back propagation ANN model


TEKE Ç., AKKURT İ., ARSLANKAYA S., Ekmekci I., Gunoglu K.

RADIATION PHYSICS AND CHEMISTRY, vol.202, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 202
  • Publication Date: 2023
  • Doi Number: 10.1016/j.radphyschem.2022.110558
  • Journal Name: RADIATION PHYSICS AND CHEMISTRY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, EMBASE, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Keywords: Gamma ray spectrum, 22Na source, ANN, Back propagation algorithm, ARTIFICIAL NEURAL-NETWORK, PHOTON ATTENUATION COEFFICIENTS, RADIATION SHIELDING PROPERTIES, NATURAL RADIOACTIVITY, CONCRETE, NEUTRON, BARITE, TRANSMISSION, ABSORPTION, ALUMINUM
  • Süleyman Demirel University Affiliated: Yes

Abstract

The radiation has been used in a variety of different fields since its discovery and thus its measurement becomes vital in these industries. Different type detector may be used to measure gamma rays depends on the purposes of measurements. Gamma ray energy spectrum is an important to determine either elemental analysing of a sample or radiation shielding purposes. On the other hand, Artificial Neural Network (ANN) may be used to predict and analysing of gamma-ray spectrum. In this study, gamma ray spectrum from 22Na source detected in NaI (Tl) detector was estimated by ANN. There have been installed ten different ANN models to find the network structure that produces the best predictive value for the gamma ray spectrum NaI (Tl) Detector. Estimation study has been continued with the ANN model with be possessed of lowest error value. ANN model was created by using energy, distance and gamma-rays energy spectrum (called Io) values. In the ANN model developed using the feed forward back propagation algorithm, were used artificial neurons two in the input layer, ten in the hidden layer and one in the output layer. For the case of present work, the experimental data was used 70% for education, 20% for validation and 10% for testing. The estimated values obtained with the ANN model were compared with the experimental results and a good correlation has been found between them (R2 = 0.99).