A scaling phenomenon for the cross section of fragment produced in projectile fragmentation reactions

Song, Yushou; Wei, Hui-Ling; Ma, Can

doi:10.1007/s11433-018-9364-x

Cited by 6 publications

(6 citation statements)

References 72 publications

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“…where , C and τ are free parameters, and minimizes odd-even staggering in the fragment distributions. This has been shown to be well obeyed by both neutron-rich [45] and neutron-deficient fragments [43,95,96], which provides an empirical approach to study the properties of recently produced neutron-deficient rare isotopes [97][98][99] and the validity of model predictions.…”

Section: Resultsmentioning

confidence: 96%

“…The FRACS results at 300 MeV/u have been implanted in the LISE++ toolkit [42]. The empirical formulas extracted from massive data analysis also indicate a good ability to reproduce the meas-ured data [43][44][45][46]. For extreme rare isotopes, new approaches are still required because the available models cannot provide satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

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Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks *

Wei

Chen

et al. 2022

Chinese Phys. C

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The machine learning models are constructed to predict the fragment production cross sections in projectile fragmentation (PF) reactions using the Bayesian neural network (BNN) techniques. The massive learning for the BNN models is based on the 6393 fragments from 53 measured projectile fragmentation reactions. A direct BNN model and a physical guiding BNN by FRACS parametrization (BNN + FRACS) model have been constructed to predict the fragment cross section in projectile fragmentation reactions. It is verified that the BNN and BNN + FRACS models can well reproduce the wide range of fragment production in PF reactions with incident energy from 40 MeV/u to 1 GeV/u, reaction systems of projectile nucleus from $^{40}$Ar to $^{208}$Pb and various target nucleus. The high precision of the BNN and BNN + FRACS models makes them applicable in the low production rate of extreme rare isotopes in the future PF reactions with large asymmetry of projectile nucleus in the main new generation of radioactive nuclear beam factories.

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Section: Resultsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks *

Wei

Chen

et al. 2022

Chinese Phys. C

Self Cite

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“…The network is trained with different model structures, and 2000 iteration samples are taken in each training. Because the fragment cross sections may differ by several orders of magnitude, an A-factors method [46,47] is introduced to indicate the validation results of different models, as shown in Fig. 1.…”

Section: Resultsmentioning

confidence: 99%

A Bayesian-neural-network prediction for fragment production in proton induced spallation reaction *

Peng

Wei

et al. 2020

Chinese Phys. C

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Fragment production in spallation reactions yields key infrastructure data for various applications. Based on the empirical SPACS parameterizations, a Bayesian-neural-network (BNN) approach is established to predict the fragment cross sections in proton-induced spallation reactions. A systematic investigation has been performed for the measured proton-induced spallation reactions of systems ranging from intermediate to heavy nuclei systems and incident energies ranging from 168 MeV/u to 1500 MeV/u. By learning the residuals between the experimental measurements and SPACS predictions, it is found that the BNN-predicted results are in good agreement with the measured results. The established method is suggested to benefit the related research on nuclear astrophysics, nuclear radioactive beam sources, accelerator driven systems, proton therapy, etc.

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“…The network is trained with different model structures, and 2000 iteration samples are taken in each training. Because the fragment cross sections may differs in several orders of magnitude, an A-factors method [48,49] is introduced to indicate the validation results of different models, as shown in Fig. 1.…”

Section: Resultsmentioning

confidence: 99%

A Bayesian-Neural-Network Prediction for Fragment Production in Proton Induced Spallation Reaction

Ma,

Peng,

Wei

et al. 2020

Preprint

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Fragments productions in spallation reactions are key infrastructure data for various applications. Based on the empirical parameterizations spacs, a Bayesian-neural-network (BNN) approach is established to predict the fragment cross sections in the proton induced spallation reactions. A systematic investigation have been performed for the measured proton induced spallation reactions of systems ranging from the intermediate to the heavy nuclei and the incident energy ranging from 168 MeV/u to 1500 MeV/u. By learning the residuals between the experimental measurements and the spacs predictions, the BNN predicted results are in good agreement with the measured results. The established method is suggested to benefit the related researches in the nuclear astrophysics, nuclear radioactive beam source, accelerator driven systems, and proton therapy, etc.

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A scaling phenomenon for the cross section of fragment produced in projectile fragmentation reactions

Cited by 6 publications

References 72 publications

Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks *

Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks *

A Bayesian-neural-network prediction for fragment production in proton induced spallation reaction *

A Bayesian-Neural-Network Prediction for Fragment Production in Proton Induced Spallation Reaction

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