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Identification of gamma emitting natural isotopes in environmental sample spectra: convolutional neural network approach

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Abstract

A method to analyse radio-isotope activity in a large number of low-resolution gamma-ray bands corresponding to a mix of isotopes is conferred in the current article. Analysis of overlapped low-resolution gamma-ray bands of radio-isotope mixtures selected for this the work. Machine learning is suitable for radio-isotope mixture evaluations because it uses abstract spectrum informa-tion like overlapping peak geometries and the Compton continuum. Among the most promising options for automating gamma-ray spectroscopy, pattern recognition methods like convolution neural networks are best suitable. CNNs may automate gamma-ray spectroscopy. These models simulate professional spectroscopy. These models discovered gamma-ray bands with substantial calibration error and unknown background radiation fields.

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Authors and Affiliations

  1. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

    Bharathi Paleti & G. Hanumat Sastry

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  1. Bharathi Paleti
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  2. G. Hanumat Sastry
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Correspondence to Bharathi Paleti.

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Paleti, B., Sastry, G.H. Identification of gamma emitting natural isotopes in environmental sample spectra: convolutional neural network approach. J Radioanal Nucl Chem 332, 5273–5281 (2023). https://doi.org/10.1007/s10967-023-09052-7

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  • DOI: https://doi.org/10.1007/s10967-023-09052-7

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