Effect of source size and emission time on the p–p momentum correlation function in the two-proton emission process

Nuclear Science and Techniques - Tập 31 - Trang 1-6 - 2020
Long Zhou1,2, De-Qing Fang1,3
1Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
2University of Chinese Academy of Sciences, Beijing, China
3Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai, China

Tóm tắt

The effect of source size and emission time on the proton–proton (p–p) momentum correlation function ($$C_\mathrm{pp}(q)$$) has been studied systematically. Assuming a spherical Gaussian source with space and time profile according to the function $$S(r,t)\sim \exp (-r^2/2r_{0}^{2}-t/\tau )$$ in the correlation function calculation code (CRAB), the results indicate that one $$C_\mathrm{pp}(q)$$ distribution corresponds to a unique combination of source size $$r_0$$ and emission time $$\tau $$. Considering the possible nuclear deformation from a spherical nucleus, an ellipsoidal Gaussian source characterized by the deformation parameter $$\epsilon =\Delta {R}/R$$ has been simulated. There is almost no difference of $$C_\mathrm{pp}(q)$$ between the results of spherically and ellipsoidally shaped sources with small deformation. These results indicate that a unique source size $$r_0$$ and emission time could be extracted from the p–p momentum correlation function, which is especially important for identifying the mechanism of two-proton emission from proton-rich nuclei. Furthermore, considering the possible existence of cluster structures within a nucleus, the double Gaussian source is assumed. The results show that the p–p momentum correlation function for a source with or without cluster structures has large systematical differences with the variance of $$r_{0}$$ and $$\tau $$. This may provide a possible method for experimentally observing the cluster structures in proton-rich nuclei.

Tài liệu tham khảo

M. Pfutzner, M. Karny, L.V. Grigorenkoet et al., Radioactive decays at limits of nuclear stability. Rev. Mod. Phys. 84, 567 (2012). https://doi.org/10.1103/RevModPhys.84.567 B. Blank, M. Ploszajczak, Two-proton radioactivity. Rep. Prog. Phys. 71, 046301 (2008). https://doi.org/10.1088/0034-4885/71/4/046301 E. Olsen, M. Pfuttzner, N. Birge et al., Landscape of two-proton radioactivity. Phys. Rev. Lett. 110, 222501 (2013). https://doi.org/10.1103/PhysRevLett.110.222501 K.W. Brown, R.J. Charity, L.G. Sobotka et al., Observation of long-range three-body Coulomb effects in the decay of \({^{16}\!\text{Ne}}.\) Phys. Rev. Lett. 113, 232501 (2014). https://doi.org/10.1103/PhysRevLett.113.232501 V.I. Goldansky, On neutron-deficient isotopes of light nuclei and the phenomena of proton and two-proton radioactivity. Nucl. Phys. 19, 482 (1960). https://doi.org/10.1016/0029-5582(60)90258-3 Y.T. Wang, D.Q. Fang, X.X. Xu et al., Implantation-decay method to study the \(\beta \)-delayed charged particle decay. Nucl. Sci. Tech. 29, 98 (2018). https://doi.org/10.1007/s41365-018-0438-5 Z.Q. Zhang, Y.G. Ma, Measurements of momentum correlation and interaction parameters between antiprotons. Nucl. Sci. Tech. 27, 152 (2016). https://doi.org/10.1007/s41365-016-0147-x R.A. Kryger, A. Azhari, M. Hellstrom et al., Two-proton emission from the ground state of \({^{12}\!\text{O}}.\) Phys. Rev. Lett. 74, 860 (1995). https://doi.org/10.1103/PhysRevLett.74.860 G. Raciti, G. Cardella, M. De Napoli et al., Experimental evidence of \({^{2}\!\text{He}}\) decay from \({^{18}\!\text{Ne}}\) excited states. Phys. Rev. Lett. 100, 192503 (2008). https://doi.org/10.1103/PhysRevLett.100.192503 Y.G. Ma, D.Q. Fang, X.Y. Sun et al., Different mechanism of two-proton emission from proton-rich nuclei \({^{23}\!{\text{Al}}}\) and \({^{22}\!\text{Mg}}.\) Phys. Lett. B 743, 306 (2015). https://doi.org/10.1016/j.physletb.2015.02.066 M.A. Lisa, C.K. Gelbke, P. Decowski et al., Observation of lifetime effects in two-proton correlations for well-characterized sources. Phys. Rev. Lett. 71, 2863 (1993). https://doi.org/10.1103/PhysRevLett.71.2863 G. Verde, A. Chbihi, R. Ghetti et al., Correlations and characterization of emitting sources. Eur. Phys. J. A 30, 81 (2006). https://doi.org/10.1140/epja/i2006-10109-6 W.A. Zajc, J.A. Bistirlich, R.R. Bossingham et al., Two-pion correlations in heavy ion collisions. Phys. Rev. C 29, 2173 (1984). https://doi.org/10.1103/PhysRevC.29.2173 D.Q. Fang, Y.G. Ma, X.Y. Sun et al., Proton–proton correlations in distinguishing the two-proton emission mechanism of \({^{23}\!{\text{Al}}}\) and \({^{22}\!\text{ Mg}}.\) Phys. Rev. C 94, 044621 (2016). https://doi.org/10.1103/PhysRevC.94.044621 S. Pratt, J. Sullivan, H. Sorge et al., Testing transport theories with correlation measurements. Nucl. Phys. A 566, 103c (1994). https://doi.org/10.1016/0375-9474(94)90614-9 M. Aygun, Z. Aygun, A theoretical study on different cluster configurations of the \({^{9}\!\text{Be}}\) nucleus by using a simple cluster model. Nucl. Sci. Tech. 28, 86 (2017). https://doi.org/10.1007/s41365-017-0239-2 C. Constantinou, M.A. Caprio, J.P. Vary et al., Natural orbital description of the halo nucleus \({^{6}\!\text{He}}.\) Nucl. Sci. Tech. 28, 179 (2017). https://doi.org/10.1007/s41365-017-0332-6 W. von Oertzen, M. Freer, Y. Kanada-En’yo, Nuclear clusters and nuclear molecules. Phys. Rep. 432, 43 (2006). https://doi.org/10.1016/j.physrep.2006.07.001 Y. Liu, J.J. Zhu, N. Roberts et al., Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks. Nucl. Sci. Tech. 30, 148 (2019). https://doi.org/10.1007/s41365-019-0677-0 H.K. Yang, K.C. Liang, K.J. Kang et al., Slice-wise reconstruction for low-dose cone-beam CT using a deep residual convolutional neural network. Nucl. Sci. Tech. 30, 59 (2019). https://doi.org/10.1007/s41365-019-0581-7 H.L. Zheng, X.G. Tuo, S.M. Peng et al., Determination of Gamma point source efficiency based on a back-propagation neural network. Nucl. Sci. Tech. 29, 61 (2018). https://doi.org/10.1007/s41365-018-0410-4 A. Gheziel, S. Hanini, B. Mohamedi et al., Particle dispersion modeling in ventilated room using artificial neural network. Nucl. Sci. Tech. 28, 5 (2017). https://doi.org/10.1007/s41365-016-0159-6