Unfolding positron lifetime spectra with neural networks

Pázsit, Imre; Chakarova, R.; Lindén, P.; Maurer, Frans H.J.

doi:10.1016/s0169-4332(99)00180-4

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The neural network in this case will be able to predict the inverse problem, avoiding the direct calculation of the K -1 . This approach has been used recently in thermodynamics 22 and for the inversion of positron lifetime annihilation lifetime spectra . A large number of systems have to be used in this approach and this may represent an inconvenience.…”

Section: Ill Posed Problems and Neural Networkmentioning

confidence: 99%

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Inversion of Simulated Positron Annihilation Lifetime Spectrum Using a Neural Network

Viterbo

Braga²,

Braga³

et al. 2001

J. Chem. Inf. Comput. Sci.

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Inversion of positron annihilation lifetime spectroscopy, based on a neural network Hopfield model, is presented in this paper. From a previous reported density function for lysozyme in water a simulated spectrum, without the superposition of statistical fluctuation and spectrometer resolution effects, was generated. These results were taken as the exact results from which the neural network was trained. The precision of the inverted density function was analyzed taking into account the number of neurons and the learning time of the neural network. A fair agreement was obtained when comparing the neural network results with the exact results. For example, the maximum of the density function, with a precision of 0.4% for the percentual relative error, was obtained for 64 neurons.

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Section: Ill Posed Problems and Neural Networkmentioning

confidence: 99%

“…This approach has been used recently in thermodynamics 22 and for the inversion of positron lifetime annihilation lifetime spectra. 23 A large number of systems have to be used in this approach and this may represent an inconvenience.…”

Section: Ill Posed Problems and Neural Networkmentioning

confidence: 99%

Inversion of Simulated Positron Annihilation Lifetime Spectrum Using a Neural Network

Viterbo

Braga²,

Braga³

et al. 2001

J. Chem. Inf. Comput. Sci.

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Artificial neural network as an effective tool to calculate parameters of positron annihilation lifetime spectra

Pietrow,

Miaskowski

2023

Journal of Applied Physics

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The paper presents the application of the multi-layer perceptron regressor model for predicting the parameters of positron annihilation lifetime spectra using the example of alkanes in the solid phase. Good agreement of calculation results was found when the approach is compared with the commonly used methods, e.g., LT. The presented method can be used as an alternative quick and accurate tool for the decomposition of positron annihilation lifetime spectroscopy (PALS) spectra in general. The advantages and disadvantages of this new method are discussed. We show the preliminary results where the trained network can give better outcomes than the results yielded by programs based on an analysis of a single PALS spectrum.

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Unfolding positron lifetime spectra with neural networks

Cited by 2 publications

References 9 publications

Inversion of Simulated Positron Annihilation Lifetime Spectrum Using a Neural Network

Inversion of Simulated Positron Annihilation Lifetime Spectrum Using a Neural Network

Artificial neural network as an effective tool to calculate parameters of positron annihilation lifetime spectra

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