Application of artificial neural networks for predicting the isotopic composition of high burn-up solid plutonium sample using the 90–105 keV gamma-spectrum region.

Based on this observation, 4500 epoch is selected as the optimum epoch number for the present ANN analysis for predicting Pu IC. 4.2 Optimization of ANN configuration There is no thumb rule for designing the ANN structure based on the input - output data dimension. Keywords: artificial neural network; gamma spectroscopy; high burn-up; plutonium EN artificial neural network gamma spectroscopy high burn-up plutonium 323 332 10 05/12/22 20220501 NES 220501 1 Introduction Till date, 20 Isotopes of Plutonium (Pu) with mass number 228 to 247 had been discovered. The isotope ratio measurement of Pu isotopes is preferred with thermal ionization mass spectrometer (TIMS) which offers and accuracy and precision better than 0.1% but the technique requires exhaustive sample preparation [[2]]. Application of artificial neural networks for predicting the isotopic composition of high burn-up solid plutonium sample using the 90-105 keV gamma-spectrum region. [Extracted from the article]