Production of the most neutron-deficient Zn isotopes by projectile fragmentation of 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mi>Kr</mml:mi><mml:mprescripts /><mml:none /><mml:mn>78</mml:mn></mml:mmultiscripts></mml:math>

Kubiela, A.; Suzuki, Hiroshi; Tarasov, O.; Pfützner, M.; Ahn, D. S.; Baba, H.; Bezbakh, A. N.; Ciemny, A. A.; Dominik, W.; Fukuda, N.; Giska, A.; Grzywacz, R.; Ichikawa, Y.; Janas, Z.; Janiak, Ł.; Kamiński, G.; Kawata, Kenji; Kubo, T.; Madurga, M.; Mazzocchi, C.; Nıshıbata, H.; Pomorski, M.; Shimizu, Y.; Sokołowska, N.; Suzuki, D.; Szymkiewicz, P.; Świercz, Andrzej; Tajima, M.; Takamine, A.; Takeda, Hiroyuki; Takeuchi, Yuki; Thornsberry, C.; Ueno, H.; Yamazaki, H.; Yokoyama, R.; Yoshida, Kôichi

doi:10.1103/physrevc.104.064610

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…Predictions for the extreme rare isotopes are also veri- fied. At the RIKEN-RIBF, highly neutron-rich Ca have been measured in the 345 MeV/u 70 Zn + 9 Be reaction [101], and highly neutron-deficient Zn have been measured in the 345 MeV/u 78 Kr + 9 Be reaction [102]. Predictions for Ca production have been performed [45,46] using FRACS and other empirical methods, which are shown to be in good agreement with the measured data.…”

Section: Resultsmentioning

confidence: 89%

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: 89%

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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“…It is intrinsic in the exotic term that such nuclei are likely short-lived and have very low production rates. Their production cross-section can be as low as a few femtobarns with rates of the order of single ions/day (Kubiela et al (2021)). In the laboratory, proton-rich isotopes can be produced by fusion-evaporation, fragmentation or spallation reactions, each of which has its optimum range of applications.…”

Section: Methods Of Productionmentioning

confidence: 99%

Nuclei near and at the proton dripline

Pfützner¹,

Mazzocchi²

2022

Preprint

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Nuclei in the vicinity of the proton dripline and beyond it are a fascinating realm within the chart of nuclei. In this chapter the main phenomena that characterise this domain and are not to be found elsewhere are explored. While moving away from the β -stability valley towards the proton dripline, phenomena like very exotic decay modes such as β -delayed (multi-) particle emission, and proton-, or two-proton radioactivity are encountered. Landmark nuclei are here two isotopes with magic proton and neutron numbers, 48 Ni and 100 Sn. Moreover, proton-rich nuclei (N < Z) display other interesting features, like breaking of isospin symmetry with consequent asymmetry in the energy spectra between mirror nuclei, the so-called Thomas-Ehrmann shift, or the phenomenon of proton-halo. Last but not least, nuclei close to the proton dripline play a very important role in nucleosynthesis, since they take part in the rapid-proton capture process and their properties are crucial in defining the flow followed and its termination close to 100 Sn.

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Nuclei Near and at the Proton Dripline

Pfützner

Mazzocchi

2022

Handbook of Nuclear Physics

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Production of the most neutron-deficient Zn isotopes by projectile fragmentation of Kr78

Cited by 7 publications

References 42 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 *

Nuclei near and at the proton dripline

Nuclei Near and at the Proton Dripline

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