logo

Improved nuclear mass formula with an additional term from the Fermi gas model

NUCLEAR PHYSICS AND INTERDISCIPLINARY RESEARCH

Improved nuclear mass formula with an additional term from the Fermi gas model

Xiao-Yu Xu
Li Deng
Ai-Xi Chen
Hang Yang
Amir Jalili
Han-Kui Wang
Nuclear Science and TechniquesVol.35, No.5Article number 91Published in print May 2024Available online 03 Jun 2024
54308

Nuclear mass is a fundamental property of nuclear physics and a necessary input in nuclear astrophysics. Owing to the complexity of atomic nuclei and nonperturbative strong interactions, conventional physical models cannot completely describe nuclear binding energies. In this study, the mass formula was improved by considering an additional term from the Fermi gas model. All nuclear masses in the Atomic Mass Evaluation Database were reproduced with a root-mean-square deviation (RMSD) of 1.86 MeV (1.92 MeV). The new mass formula exhibits good performance in the neutron-rich nuclear region. The RMSD decreases to 0.393 MeV when the ratio of the neutron number to the proton number is 1.6.

Nuclear mass formulaNeutron-rich nucleiFermi-gas model
1

Introduction

The precise calculation of the nuclear mass is of profound significance in the fields of nuclear physics and astrophysics [1]. Nuclear physics encompasses the analysis of over 3,000 measured nuclear masses, enabling the exploration of nuclear symmetry energy [2-5]. Synthesis of superheavy nuclei [6-8] has attracted increasing attention in recent years [9]. Accurate predictions of shell corrections and α-decay energies of superheavy nuclei are urgently required to synthesize new superheavy nuclei [10]. Concurrently, nuclear symmetry energy is believed to be crucial for correctly interpreting nuclear masses [9, 11], influencing both the nuclear structure and dynamic behavior of nuclear reactions [10]. Several nuclear mass models have been developed to achieve root-mean-square deviations (RMSDs) ranging from several hundred keV to a few MeV for all known nuclear masses.

Unmeasured masses are typically predicted by using global nuclear mass models that incorporate various physical aspects. The model parameters are determined either by employing the available measured masses [12-15] or by adopting local mass relations based on the measured masses of neighboring nuclei. Global nuclear mass models, such as the finite range droplet model (FRDM) [12, 16], extended Bethe-Weizsäcker (BW2) formula [17], Weizsäcker-Skyrme (WS) mass models [2, 9, 10], nonrelativistic Hartree-Fock-Bogoliubov (HFB) approach with the Skyrme energy-density functional (EDF) [18], Gogny forces and the relativistic mean-field (RMF) model [19], and Duflo-Zuker (DZ) mass models [20], successfully reproduce measured masses with an accuracy at the level of 300-600 keV. Although the predictions from these global mass models closely align with known masses, substantial differences arise when addressing neutron drip lines and superheavy nuclei. These differences highlight the need to consider additional physics and information regarding nuclear forces in mass models for accurate predictions in these regions.

In addition to global mass models, local mass relations such as the isobaric multiplet mass equation (IMME) [21], Garvey-Kelson (GK) relations [22-24], and residual proton-neutron interactions [25-27] can be employed to predict unmeasured masses. However, it has been observed that using these local mass relations to iteratively predict masses leads to a rapid increase in the intrinsic error [28]. This is attributed to two main factors: 1. The local mass relations are only approximately satisfied for known masses and 2. the previously predicted masses are incorporated in each new iteration, resulting in the accumulation of systematic error [29]. The mass relations of mirror nuclei operate under the assumption that nuclear interactions conserve isospin symmetry [30-32]. Recently, these relationships were proven to be remarkably accurate. Under this assumption, the mass difference between the two mirror nuclei is determined by the Coulomb interaction and constant values associated with the neutron-proton mass difference [31].

Recently, artificial neural networks (ANNs), one of the most powerful machine-learning methods, have been successfully applied in nuclear physics studies [33-37]. In nuclear physics, Bayesian neural networks (BNNs) have been employed to minimize mass residuals between theory and experiment. There has been notable enhancement in the mass prediction of several theoretical models following BNN refinement [38-40]. For instance, the RMSD of the liquid-drop model decreases from 3 to 0.8 MeV.

This study focused on improving the BW2 mass formula, with attention directed toward the Fermi gas model [41] and higher order term of the symmetry energy [42-44]. The results indicate that the RMSD of the modified BW2 mass formula was reduced by 3%, representing a significant improvement in the accuracy of calculating neutron-rich nuclei [45-49].

The remainder of this paper is organized as follows. Section 2 provides an explanation of the BW2 mass formula and the derivation of the correction term. The results and discussion are presented in Sect. 3, and a summary and future prospects are provided in Sect. 4.

2

Nuclear mass model

2.1
Mass formula

The mass formula BW2 is based on the classical liquid-drop model and incorporates additional physical terms for a more comprehensive analysis. In this formula, the exchange Coulomb term αxCZ43A13, Wigner term αW|NZ|A, surface symmetry term αst(NZ)2A43, pairing term αpδ(N,Z)A12, curvature term αRA13, and shell effect term αmP+βmP2 have been added. It should be noted that the shell effect term contains two parameters. The model used in this study is obtained from Refs. [17]: BEBW2=αvA+αsA23+αCZ2A13+αt(NZ)2A+αxCZ43A13+αW|NZ|A+αst(NZ)2A43+αpδ(N,Z)A12+αRA13+αmP+βmP2, (1) where αi denotes the free parameters determined by fitting the experimental nuclear masses. δ(N,Z) is given by δ(N,Z)=(1)N+(1)Z2; (2) that is, δ(N,Z) takes the value +1 for even-even nuclei, -1 for odd-odd nuclei, and 0 for odd nuclei. P is given by P=νnνpνn+νp, (3) where νn and νp are the number of valence nucleons (the difference between the actual nucleon numbers N and Z and the nearest magic numbers, respectively). To calculate P, the magic numbers were canonical 2, 8, 20, 28, 50, 82, 126, and 184 for both neutrons and protons.

The latest and most comprehensive database of nuclear masses is the Atomic Mass Evaluation Database, commonly known as AME2020 [50]. This tabulation served as the experimental data for this study. The pertinent input comprises a list of the measured binding energies of the nuclei acquired by multiplying the tabulated binding energy per nucleon by the mass number (A).

2.2
Improved nuclear mass formula

In this section, we demonstrate that the nucleon binding energies can be understood using the Fermi gas model. Moreover, the primary terms of the semiempirical mass formula arise naturally from the model. Protons and neutrons, including the nucleus, are conceptualized in the Fermi gas model to form two independent nucleon systems. It is assumed that the nucleons can move freely throughout the entire nuclear volume within the constraints imposed by Pauli’s principles. The potential participating in each nucleon is a superposition of the potentials generated by other nucleons.

A system of such fermions is treated as a degenerate gas, and its temperature is below the Fermi temperature, which is defined as ΘF=EFKB, where EF is the Fermi energy and KB is the Boltzmann constant. The Fermi energy at 0 K is given by EF=h22m(3n8π)2/3, (4) where m and n represent the mass and number density of the fermions, respectively. According to the Fermi gas model, the total kinetic energy of nucleons is E(Z,N)=NEN+ZEZ=310m2r02(9π4)2/3(N5/3+Z5/3A2/3). (5) It was assumed that the radii of the proton and neutron potential wells are identical. Let Z - N = δ and Z+N=A. By using a binomial expansion, the following re-expression of Eq. (5) near N = Z can be obtained: E(Z,N)=310m2r02(9π8)23[A+59(NZ)2A+5243(NZ)4A3+], (6) which gives us the functional dependence on the neutron surplus. The first term contributes to the volume term in the mass formula, and the second term describes the correction resulting from NZ. This so-called symmetry energy increases with the square of the neutron surplus, and the binding energy shrinks accordingly. The third term is the higher order term of the symmetry energy used to improve the semiempirical mass formula. However, the associated coefficients were almost half of the actual values. This deviation arises because only the contributions of kinetic energy and potential energy have not been considered in this calculation.

By adding the fourth-order term of the symmetry energy to the BW2 formula, we obtain the BW3 formula BEBW3=αvA+αsA23+αCZ2A13+αt(NZ)2A+αxCZ43A13+αW|NZ|A+αst(NZ)2A43+αpδ(N,Z)A12+αRA13+αmP+βmP2+b(NZ)4A3, (7) where b=1162(9π8)232mr02. (8)

3

Discussion

3.1
Determination of the additional term coefficient

The criteria for evaluating the quality of a semiempirical mass formula hinge on its capacity to embody clear physical principles, minimize dependency on extraction parameters, yield superior calculation results, and clarify the nuclear properties relevant to the nuclear mass. The goodness of fit was assessed using the RMSD of the extraction from the measured binding energies as follows: RMSD=i(MiFi)2n, (9) where Mi denotes the theoretical value, Fi is the experimental value, and n is the total number of data points.

As discussed above, the value of b calculated using Eq. (8) differs from the experimental fitting value. To obtain an accurate value of b, the following approach was taken: First, Eq. (8) was used to determine the range of b; second, the RMSD of the BW3 mass formula was calculated over the range of b values, with the optimal b value corresponding to the smallest RMSD. The calculations show that the RMSD reaches its minimum value of 1.86 MeV at b=-1.3 MeV. Consequently, the value b=-1.3 MeV was set in the BW3 mass formula. The fitted coefficients of the semiempirical mass formula in Eq. (7) are provided in Table 1, with the first eleven items aligning with the BW2 mass formula.

Table 1
Fit values (in MeV) of the coefficients of the semiempirical mass formula of Eq. (7)
αv αs αC αt αxC αW
16.58 -26.95 -0.774 -31.51 2.22 -43.40
αst αp αR αm βm b
55.62 9.87 14.77 -1.90 0.140 -1.30
Show more

To verify the accuracy of the b values, a group of nuclides was randomly selected to create a comparison diagram of the differences between the experimental and calculated values of their specific binding energies. As shown in Fig. 1, the horizontal coordinate represents the range of b values and the vertical coordinate represents the deviation of the specific binding energy between the predicted value of the BW3 mass formula and the experimental value. The BW3 mass formula degenerates into the BW2 mass formula at b=0 MeV. It can be observed that all curves for the selected nuclides lie below the origin of coordinates, indicating that the fit of the BW2 mass formula is not ideal. Moreover, if the deviation between the theoretical and experimental values is small, a negative value of b should be adopted. This is because the intersection of the curve with the axis was negative. Figure 1 clearly shows that, when the b value changes between -3 and 3 MeV, the overall trend of the deviation curve is from positive to negative as b traverses from a negative to a positive value. When b=-1.3 MeV, the difference in the specific binding energies approaches zero. These results verify that the selection of b is significant.

Fig. 1
(Color online) Deviation of the specific binding energy between the predicted value of the BW3 formula and the experimental value
pic

The nuclide curves for 85Kr, 158Tb, 165Lu, and 217 U shown in Fig. 1 are relatively smooth. Although the changes were smooth, they still followed the trend of positive-to-negative deviations in the specific binding energy. The results demonstrate that some nuclides are insensitive to changes in the b value when the BW3 mass formula is used for the prediction. The steepest curve corresponded to 138Sn. At b=0, the deviation was the largest among the selected nuclides. The BW3 mass formula significantly reduces this deviation. For 85Kr, 94Kr, 158Tb, and 168Tb, we can clearly observe that the deviation in the specific binding energy is proportional to the neutron number. When the neutron number was small, the difference changed more gently, and vice versa, becoming steeper. Evidently, an additional term that depends on the difference between the neutron and proton numbers can improve the accuracy of the model for neutron-rich nuclei.

3.2
Effects of the higher order term

Figure 2 shows the difference (Z,N8) between the experimental values of the binding energy and those calculated using the BW2 and BW3 mass formulas. The dotted crosses in the figure indicate that the BW3 mass formula outperforms the BW2 mass formula in this context. By calculating the RMSD for both formulas (i.e., DrmsBW2=1.92MeV and DrmsBW3=1.86MeV), it is clear that the RMSD of the BW3 mass formula is 3% lower than that of the BW2 mass formula. To analyze the impact of the additional term, a difference distribution between the experimental values of the binding energy and the values calculated from the BW2 and BW3 mass formulas was developed. Local enlargements of N≈95, Z≈55 and N≈140, Z≈83 are shown in Figs. 2(a) and 2(b). It is evident from the figures that, for N≈95 and Z≈55, the original yellow and orange grids in Fig. 2(a) are replaced by orange and green grids, respectively, in Fig. 2(b). For N≈140 and Z≈83, the original yellow, orange, and red grids in Fig. 2(a) are replaced by green, yellow, and orange grids in Fig. 2(b).

Fig. 2
(Color online) Deviation from the experimental binding energy of the values predicted by the (a) BW2 and (b) BW3 mass formulas
pic

The transition from red to green in the color levels set in Fig. 2 indicates a gradual decrease in the difference. This observation shows that the deviation between the predicted value of the nucleus and the experimental binding energy is reduced by the BW3 mass formula. The dotted line crosses the β-stability line, indicating that it lies within a neutron-rich mass region. This observation verified the hypothesis that an additional term can reduce the deviation between the experimental and calculated binding energies of neutron-rich nuclei. Simultaneously, for nuclei with magic numbers of protons or neutrons, the deviation between the predictions of the two mass formulas and the experimental data was significant. In the case of double magic nuclei (in which both the neutron and proton numbers are magic numbers), the deviation is particularly pronounced.

To investigate the influence of additional terms on the specific binding energy of neutron-rich nuclei, a group of nuclides and their isotopic chains were randomly selected to calculate the difference between the experimental and calculated specific binding energies obtained from the BW2 and BW3 mass formulas. The isotopic chains of nine types of nuclides were randomly selected, and the deviations between the BW2 and BW3 mass formula predictions and experimental specific binding energies were compared. As shown in Fig. 3, the horizontal coordinate is the neutron number, and the vertical coordinate is the deviation of the specific binding energy. The red curve shows the deviation between the experimental value of the specific binding energy and BW2Theo (calculated value), whereas the black curve shows the deviation between the experimental value of the specific binding energy and the calculated value obtained from BW3Theo. As the neutron number increases, the BW3 mass formula with the additional term (black curve in the figure) improves at the neutron-rich nuclei and exhibits the same trend as that of the BW2 curve. The RMSD decreases to 0.393 MeV when the ratio of the neutron number to the proton number (N/P) is 1.6 (RMSD of 2.082 MeV versus 2.475 MeV).

Fig. 3
(Color online) Differences between the experimental values of the isotope-specific binding energy and the values calculated with the BW2 and BW3 formulas
pic

As the neutron number increases, BW3Theo and BW2Theo gradually deviate, and the difference between them increases, particularly for neutron-rich nuclei. The black curve consistently remained above the red curve and approached zero. This trend reveals that the BW3 mass formula can significantly reduce the deviation in the specific binding energies of neutron-rich nuclei. This observation confirms that the BW3 mass formula significantly enhances the calculation accuracy and is more reliable for predicting the mass of neutron-rich nuclei. In Fig. 3, it is apparent that, with a gradual increase in the proton number, the neutron number corresponding to the peak value of the isotope chain curve becomes the magic number. This further indicates that there is a substantial deviation between the predictions of the nuclear mass formula, with the neutron or proton number being the magic number, and the experimental values, implying room for improvement in the mass formula.

3.3
Isobaric elements

To further investigate the predictions of the BW3 mass formula, a comparison graph was developed for six isobaric elements with masses ranging from A=100 to A=150. The graphs in Fig. 4 show the neutron numbers on the horizontal axis and the difference in the specific binding energies on the vertical axis. The red lines represent the deviation between the experimental values of specific binding energy and the calculated values of the BW3 mass formula, and the black lines represent the deviation between the experimental values of specific binding energy and the calculated values of the BW2 mass formula. These six subgraphs reveal that, when the neutron number is small, the two curves almost coincide. This verifies that the difference between the two equations was small at this time; however, as the neutron number increased, the difference between the two equations gradually increased. When the curve intersects the axis, the red curves are completely above the black curves, except for the curve shown in Fig. 4(d), indicating that the calculated value of the BW3 mass formula here is closer to the experimental value and is more accurate than the value of the BW2 mass formula.

Fig. 4
(Color online) Differences between the experimental values of the isobaric-specific binding energy and the values calculated with the BW2 and BW3 mass formulas
pic

Because the mass number A is fixed, as shown in Fig. 4, the proton number decreases as the neutron number increases, and the neutron number minus the proton number has a common difference of 2. In particular, the curves in subgraphs in Figs. 4(c) and 1(d) exhibit the steepest trends. In Fig. 4(c), when Z=46 and N=74, |N-Z|=28, and the difference in specific binding energy is the closest to zero. The BW3 mass formula is superior to the BW2 mass formula. However, there are also cases in which the difference increases and decreases dramatically, such as N=68 to N=69 and N=71 to N=72. Similarly, in Fig. 4(d), at Z=57 and N=77, |N-Z|=16, and the difference in specific binding energy is the closest to zero. However, the calculation accuracy of the BW3 mass formula is close to that of the BW2 mass formula. From N=77 to N=81, the difference in the specific binding energies increased sharply, and the calculation accuracy of the BW3 mass formula was inferior to that of the BW2 mass formula. These trends reveal that there must be a correlation between the remaining terms after removing the volume and surface terms from the mass formula.

4

Summary

In summary, we improved the nuclear mass formula by considering the fourth-order term of the symmetry energy, b(NZ)4A3, in the Fermi gas model. The additional term from the Fermi gas model improved the semiempirical mass formula as the RMSD was reduced by 3%.

The semiempirical mass formula has a symmetrical energy term (NZ)2A from the Fermi gas model. To extend the mass formula to superheavy nuclei and nuclei far from the β-stability line, we pay special attention to the higher order term b(NZ)4A3 derived from the symmetry energy. The associated coefficients were almost half of the actual values because only the contributions of the kinetic energy and potential energy were not considered in this calculation. The coefficient is derived by fitting the formula to the experimental data. The coefficient was determined when the RMSD reached a minimum value of 1.86 MeV.

To further test the model, the appearance of magic numbers in neutron-rich nuclei was examined. Our results are in good agreement with experimental and theoretical studies. This study demonstrated that our model offered good performance in a neutron-rich mass region, which is useful for rapid neutron capture in nuclear astrophysics. The RMSD decreases to 0.393 MeV when the ratio of the neutron number to the proton number is 1.6 (RMSD of 2.082 MeV versus 2.475 MeV).

References
1. D. Lunney, J. M. Pearson, C. Thibault,

Recent trends in the determination of nuclear masses

. Rev. Mod. Phys 75, 1021 (2003). https://doi.org/10.1103/RevModPhys.75.1021
Baidu ScholarGoogle Scholar
2. N. Wang, Z. Y. Liang, M. Liu et al.,

Mirror nuclei constraint in nuclear mass formula

. Phys. Rev. C 82, 044304 (2010). https://doi.org/10.1103/PhysRevC.82.044304
Baidu ScholarGoogle Scholar
3. B. A. Li, L. W. Chen, C. M. Ko,

Recent progress and new challenges in isospin physics with heavy-ion reactions

. Phys. Rep 464, 113-281 (2008). https://doi.org/10.1016/j.physrep.2008.04.005
Baidu ScholarGoogle Scholar
4. W. H. Ye, Y. B. Qian, Z. Z. Ren,

Accuracy versus predictive power in nuclear mass tabulations

. Phys. Rev. C 106, 024318 (2022). https://doi.org/10.1103/PhysRevC.106.024318
Baidu ScholarGoogle Scholar
5. Y. P. Cao, D. H. Liu, Y. B. Qian et al.,

Uncertainty analysis for the nuclear liquid drop model and implications for the symmetry energy coefficients

. Phys. Rev. C 105, 034304 (2022). https://doi.org/10.1103/PhysRevC.105.034304
Baidu ScholarGoogle Scholar
6. Y. T. Oganessian, F. S. Abdullin, P. D. Bailey et al.,

Synthesis of a New Element with Atomic Number Z=117

. Phys. Rev. L 104, 142502 (2010). https://doi.org/10.1103/PhysRevLett.104.142502
Baidu ScholarGoogle Scholar
7. S. Ćwiok, P. -H. Heenen, W. Nazarewicz,

Shape coexistence and triaxiality in the superheavy nuclei

. Nature 433, 705-709 (2005). https://doi.org/10.1038/nature03336
Baidu ScholarGoogle Scholar
8. A. Sobiczewski, K. Pomorski,

Description of structure and properties of superheavy nuclei

. Prog. Part. Nucl. Phys 58, 292-349 (2007). https://doi.org/10.1016/j.ppnp.2006.05.001
Baidu ScholarGoogle Scholar
9. N. Wang, M. Liu, X. Wu,

Modification of nuclear mass formula by considering isospin effects

. Phys. Rev. C 81, 044322 (2010). https://doi.org/10.1103/PhysRevC.81.044322
Baidu ScholarGoogle Scholar
10. M. Liu, N. Wang, Y. Deng et al.,

Further improvements on a global nuclear mass model

. Phys. Rev. C 84, 014333 (2011). https://doi.org/10.1103/PhysRevC.84.014333
Baidu ScholarGoogle Scholar
11. N. Nikolov, N. Schunck, W. Nazarewicz et al.,

Surface symmetry energy of nuclear energy density functionals

. Phys. Rev. C 83, 034305 (2011). https://doi.org/10.1103/PhysRevC.83.034305
Baidu ScholarGoogle Scholar
12. G. Audi, A. H. Wapstra,

The 1995 update to the atomic mass evaluation

. Nucl. Phys. A 595, 409-480 (1995). https://doi.org/10.1016/0375-9474(95)00445-9
Baidu ScholarGoogle Scholar
13. G. Audi, A. H. Wapstra, C. Thibault,

The Ame2003 atomic mass evaluation: (II). Tables, graphs and references

. Nucl. Phys. A 729, 337-676 (2003). https://doi.org/10.1016/j.nuclphysa.2003.11.003
Baidu ScholarGoogle Scholar
14. N. Wang, M. Liu, L. Ou et al.,

Properties of nuclear matter from macroscopic-microscopic mass formulas

. Phys. Lett. B 751, 553-558 (2015). https://doi.org/10.1016/j.physletb.2015.11.006
Baidu ScholarGoogle Scholar
15. N. Wang, M. Liu, X. Z. Wu et al.,

Surface diffuseness correction in global mass formula

. Phys. Lett. B 734, 215-219 (2014). https://doi.org/10.1016/j.physletb.2014.05.049
Baidu ScholarGoogle Scholar
16. P. Moller, J. R. Nix, W.D. Myers et al.,

Nuclear Ground-State Masses and Deformations

. Atom. Data Nucl. Data Tabl 59, 185-381 (1995). https://doi.org/10.1006/adnd.1995.1002
Baidu ScholarGoogle Scholar
17. M. W. Kirson,

Mutual influence of terms in a semi-empirical mass formula

. Nucl. Phys. A 798, 29-60 (2008). https://doi.org/10.1016/j.nuclphysa.2007.10.011
Baidu ScholarGoogle Scholar
18. S. Goriely, N. Chamel, J. M. Pearson,

Skyrme-Hartree-Fock-Bogoliubov Nuclear Mass Formulas: Crossing the 0.6 MeV Accuracy Threshold with Microscopically Deduced Pairing

. Phys. Rev. Lett. 102, 152503 (2009). https://doi.org/10.1103/PhysRevLett.102.152503
Baidu ScholarGoogle Scholar
19. B. H. Sun, J. Meng,

Challenge on the Astrophysical R-Process Calculation with Nuclear Mass Models

. Chin. Phys. Lett 25, 2429-2431 (2008). https://doi.org/10.1088/0256-307X/25/7/027
Baidu ScholarGoogle Scholar
20. J. Duflo, A. P. Zuker,

Microscopic mass formulas

. Phys. Rev. C 52, R23-R27 (1995). https://doi.org/10.1103/PhysRevC.52.R23
Baidu ScholarGoogle Scholar
21. W. E. Ormand,

Mapping the proton drip line up to A=70

. Phys. Rev. C 55, 2407-2417 (1997). https://doi.org/10.1103/PhysRevC.55.2407
Baidu ScholarGoogle Scholar
22. J. Barea, A. Frank, J. G.Hirsch et al.,

Garvey-Kelson relations and the new nuclear mass tables

. Phys. Rev. C 77, 041304 (2008). https://doi.org/10.1103/PhysRevC.77.041304
Baidu ScholarGoogle Scholar
23. J. G. Hirsch, A. Frank, J. Barea et al.,

Bounds on the presence of quantum chaos in nuclear masses

? Eur. Phys. J. A 25, 75-78 (2005). https://doi.org/10.1140/epjad/i2005-06-050-0
Baidu ScholarGoogle Scholar
24. C. Ma, M. Bao, Z. M. Niu et al.,

New extrapolation method for predicting nuclear masses

. Phys. Rev. C 101, 045204 (2020). https://doi.org/10.1103/PhysRevC.101.045204
Baidu ScholarGoogle Scholar
25. G. J. Fu, H. Jiang, Y. M. Zhao et al.,

Nuclear binding energies and empirical proton-neutron interactions

. Phys. Rev. C 82, 034304 (2010). https://doi.org/10.1103/PhysRevC.82.034304
Baidu ScholarGoogle Scholar
26. H. Jiang, G. J. Fu, Y. M. Zhao et al.,

Nuclear mass relations based on systematics of proton-neutron interactions

. Phys. Rev. C 82, 054317 (2010). https://doi.org/10.1103/PhysRevC.82.054317
Baidu ScholarGoogle Scholar
27. X. Yin, R. Shou, Y. M. Zhao,

Atomic masses of nuclei with neutron numbers N < 126 and proton numbers Z>82

. Phys. Rev. C 105, 064304 (2022). https://doi.org/10.1103/PhysRevC.105.064304
Baidu ScholarGoogle Scholar
28. I. O. Morales, J. C. LópezVieyra, J. G. Hirsch et al.,

How good are the Garvey-Kelson predictions of nuclear masses

? Nucl. Phys. A 828, 113-124 (2009). https://doi.org/10.1016/j.nuclphysa.2009.07.001
Baidu ScholarGoogle Scholar
29. N. Wang, M. Liu,

Nuclear mass predictions with a radial basis function approach

. Phys. Rev. C 84, 051303 (2011). https://doi.org/10.1103/PhysRevC.84.051303
Baidu ScholarGoogle Scholar
30. Y. Y. Zong, C. Ma, M. Q. Lin et al.,

Mass relations of mirror nuclei for both bound and unbound systems

. Phys. Rev. C 105, 034321 (2022). https://doi.org/10.1103/PhysRevC.105.034321
Baidu ScholarGoogle Scholar
31. C. Ma, Y. Y. Zong, Y. M. Zhao et al.,

Mass relations of mirror nuclei with local correlations

. Phys. Rev. C 102, 024330 (2020). https://doi.org/10.1103/PhysRevC.102.024330
Baidu ScholarGoogle Scholar
32. Y. Y. Zong, C. Ma, Y. M. Zhao et al.,

Mass relations of mirror nuclei

. Phys. Rev. C 102, 024302 (2020). https://doi.org/10.1103/PhysRevC.102.024302
Baidu ScholarGoogle Scholar
33. E. Alhassan, D. Rochman, A. Vasiliev et al.,

Iterative Bayesian Monte Carlo for nuclear data evaluation

. Nucl. Sci. Tech. 33, 50 (2022). https://doi.org/10.1007/s41365-022-01034-w
Baidu ScholarGoogle Scholar
34. Y. Y. Li, F. Zhang, and J. Su,

Improvement of the Bayesian neural network to study the photoneutron yield cross sections

. Nucl. Sci. Tech. 33, 135 (2022). https://doi.org/10.1007/s41365-022-01131-w
Baidu ScholarGoogle Scholar
35. Z. P. Gao, Y. J. Wang, Q. F. Li et al.,

Machine learning the nuclear mass

. Nucl. Sci. Tech. 32, 109 (2021). https://doi.org/10.1007/s41365-021-00956-1
Baidu ScholarGoogle Scholar
36. X. C. Ming, H. F. Zhang, R. R. Xu et al.,

Nuclear mass based on the multi-task learning neural network method

. Nucl. Sci. Tech. 33, 48 (2022). https://doi.org/10.1007/s41365-022-01031-z
Baidu ScholarGoogle Scholar
37. M. R. Mumpower, T. M. Sprouse, A. E. Lovell et al.,

Physically interpretable machine learning for nuclear masses

. Phys. Rev. C 106, L021301 (2022). https://doi.org/10.1103/PhysRevC.106.L021301
Baidu ScholarGoogle Scholar
38. R. Utama, J. Piekarewicz, H. B. Prosper,

Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach

. Phys. Rev. C 93, 014311 (2016). https://doi.org/10.1103/PhysRevC.93.014311
Baidu ScholarGoogle Scholar
39. R. Utama, J. Piekarewicz,

Refining mass formulas for astrophysical applications: A Bayesian neural network approach

. Phys. Rev. C 96, 044308 (2017). https://doi.org/10.1103/PhysRevC.96.044308
Baidu ScholarGoogle Scholar
40. Z. M. Niu, H. Z. Liang,

Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects

. Phys. Lett. B 778, 48-53 (2018). https://doi.org/10.1016/j.physletb.2018.01.002
Baidu ScholarGoogle Scholar
41. N. B. Zhang, B. J. Cai, B. A. Li et al.,

How tightly is the nuclear symmetry energy constrained by a unitary Fermi gas

? Nucl. Sci. Tech. 28, 181 (2017). https://doi.org/10.1007/s41365-017-0336-2
Baidu ScholarGoogle Scholar
42. H. Yu, D. Q. Fang, Y. G. Ma,

Investigation of the symmetry energy of nuclear matter using isospin-dependent quantum molecular dynamics

. Nucl. Sci. Tech. 31, 61 (2020). https://doi.org/10.1007/s41365-020-00766-x
Baidu ScholarGoogle Scholar
43. R. An, S. Sun, L. G. Cao et al.,

Constraining nuclear symmetry energy with the charge radii of mirror-pair nuclei

. Nucl. Sci. Tech. 34, 119 (2023). https://doi.org/10.1007/s41365-023-01269-1
Baidu ScholarGoogle Scholar
44. R. Wang, L. W. Chen,

Empirical information on nuclear matter fourth-order symmetry energy from an extended nuclear mass formula

. Phys. Lett. B 773, 62-67 (2017). https://doi.org/10.1016/j.physletb.2017.08.007
Baidu ScholarGoogle Scholar
45. R. Ogul, N. Buyukcizmeci, A. Ergun et al.,

Production of neutron-rich exotic nuclei in projectile fragmentation at Fermi energies

. Nucl. Sci. Tech. 28, 18 (2016). https://doi.org/10.1007/s41365-016-0175-6
Baidu ScholarGoogle Scholar
46. N. B. Zhang, B. A. Li,

Astrophysical constraints on a parametric equation of state for neutron-rich nucleonic matter

. Nucl. Sci. Tech. 29, 178 (2018). https://doi.org/10.1007/s41365-018-0515-9
Baidu ScholarGoogle Scholar
47. Y. Liu, Y. L. Ye,

Nuclear clustering in light neutron-rich nuclei

. Nucl. Sci. Tech. 29, 184 (2018). https://doi.org/10.1007/s41365-018-0522-x
Baidu ScholarGoogle Scholar
48. K. Yoshida,

Enhanced moments of inertia for rotation in neutron-rich nuclei

. Phys. Lett. B 834, 137458 (2022). https://doi.org/10.1016/j.physletb.2022.137458
Baidu ScholarGoogle Scholar
49. B. Jurado, H. Savajols, W. Mittig et al.,

Mass measurements of neutron-rich nuclei near the N = 20 and 28 shell closures

. Phys. Lett. B 649, 43-48 (2007). https://doi.org/10.1016/j.physletb.2007.04.006
Baidu ScholarGoogle Scholar
50. M. Wang, W. J. Huang, F. G. Kondev et al.,

The AME 2020 atomic mass evaluation (II). Tables, graphs and references*

. Chinese Phys. C 45, 030003 (2021). https://doi.org/10.1088/1674-1137/abddaf
Baidu ScholarGoogle Scholar
Footnote

The authors declare that they have no competing interests.