Abstract
The variation of absolute photopeak efficiency of an array of clover HPGe detectors as a function of γ-ray energy has been obtained using statistical model analysis. Machine learning technique has been implemented for determining the energy-dependent polynomial of photopeak efficiency. The distribution of parameters of the best-fit model has been deduced by incorporating Markov chain Monte Carlo method working with the basic framework of Bayesian analysis which also provides the correlation between the parameters. This method has the advantage of calculating the posterior probability distribution by maximizing the likelihood function of the experimental data over standard least-squares method. Excellent agreement among the results procured from machine learning, Markov chain Monte Carlo, and least-squares method has been achieved and the correct prediction of the nature of the variation extrapolation region as well as interpolation region has been discussed.
Published Version
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