\begin{document}$ A\ge40 $\end{document} and \begin{document}$ Z\ge20 $\end{document}. In the present study, we present the first application of CatBoost regression to compute nuclear charge radii. We integrated two experimental datasets with RHB-calculated point-coupling interaction (PC-X) theoretical features and extended our study range to \begin{document}$ A\ge17 $\end{document}, \begin{document}$ Z\ge8 $\end{document}. We found the best hyperparameters using Optuna’s Tree-structured Parzen Estimator (TPE) sampler with 10-fold cross-validation (CV), achieving a CV root-mean-square error (RMSE) of 0.0106 fm and hold-out RMSE of 0.0102 fm, with only three features, i.e., neutron number (N), proton number (Z), and RHB theoretical binding energy (BE), outperforming nine other ML models: random forest (RF), quantile RF (QRF), Cubist, Gaussian process regression with polynomial kernel (GPPK), multivariate adaptive regression splines (MARS), SVR, ANN, convolutional neural network (CNN), and Brussels-Skyrme-on-a-grid 3 (BSkG3). SHapley Additive exPlanations (SHAP) analysis confirms the highest global influence of BE in the model's predictions, followed by proton number and neutron number. The proposed model can accurately reproduce the \begin{document}$ N=50 $\end{document} kink and odd-even staggering effects in krypton and strontium chains. These results establish CatBoost as a robust and notably promising model for charge-radius prediction and beyond, with the potential to impact r-process modeling and future theoretical development."> Bayesian-optimized CatBoost for ground-state nuclear charge-radius prediction -
  • [1]

    P. Ring, and P. Schuck,The nuclear many-body problem(New York: Springer Science & Business Media, 2004)

  • [2]

    K. S. Krane,Introductory nuclear physics(New York: John Wiley & Sons, 1991)

  • [3]

    K. W. Ford and D. L. Hill, Ann. Rev. Nucl. Part. Sci.5, 25 (1955)

  • [4]

    H. F. Ehrenberg, R. Hofstadter, U. Meyer-Berkhoutet al., Phys. Rev.113, 666 (1959)

  • [5]

    L. R. Suelzle, M. R. Yearian, H. Crannell, Phys. Rev.162, 992 (1967)

  • [6]

    I. Angeli, Y. P. Gangrsky, K. P. Marinovaet al., J. Phys. G Nucl. Part. Phys.36, 085102 (2009)

  • [7]

    C. V. Weizsäcker, Zeitschrift für Physik.96, 431 (1935)

  • [8]

    J. Duflo, Nucl. Phys. A29, 576 (1994)

  • [9]

    J. Piekarewicz, M. Centelles, X. Roca-Mazaet al., Eur. Phys. J. A.46, 379 (2010)

  • [10]

    N. Wang, T. Li, Phys. Rev. C88, 011301 (2013)

  • [11]

    M. V. Stoitsov, J. Dobaczewski, W. Nazarewiczet al., Phys. Rev. C68, 054312 (2003)

  • [12]

    S. Goriely, N. Chamel, and J. Pearson, Phys. Rev. C.88, 024308 (2013)

  • [13]

    P. G. Reinhard, Rep. Prog. Phys.52, 439 (1989)

  • [14]

    C. Ma, Y. Y. Zong, Y. M. Zhaoet al., Phys. Rev. C104, 014303 (2021)

  • [15]

    R.-Y. Zheng, X.-X. Sun, G. F. Shenet al., Chin. Phys. C48, 014107 (2024)

  • [16]

    X. Zhang, Z. Niu, W. Sunet al., Phys. Rev. C108, 024310 (2023)

  • [17]

    K. Zhanget al., At. Data Nucl. Data Tables144, 101488 (2022)

  • [18]

    P. Choudhary, P. C. Srivastava, and P. Navrátil, Phys. Rev. C102, 044309 (2020)

  • [19]

    C. Forssén, E. Caurier, and P. Navrátil, Phys. Rev. C79, 021303 (2009)

  • [20]

    A. Boehnleinet al., arXiv: 2112.02309

  • [21]

    E. Yüksel, D. Soydaner, and H. Bahtiyar, Phys. Rev. C109, 064322 (2024)

  • [22]

    M. Wang, W. Huang, and F. G. Kondev, Chin. Phys. C45, 030003 (2021)

  • [23]

    W. J. Huang, M. Wang, F. G. Kondevet al., Chin. Phys. C45, 030002 (2021)

  • [24]

    B. Pandey and S. Giri, AIP Adv.14, 10 (2024)

  • [25]

    C. Y. Tsanget al., arXiv: 2107.13985

  • [26]

    T. Bayram, S. Akkoyun, S. Okan Karaet al., Ann. Nucl. Energy.63, 172 (2014)

  • [27]

    Z. Jin and M. Yan, Phys. Rev. C108, 014326 (2023)

  • [28]

    Q. Yuan and P. Qi, arXiv: 2508.03155

  • [29]

    H. Q. You, X. T. Heet al., Nucl. Sci. Tech.36, 1 (2025)

  • [30]

    Z. Yuan, D. Baiet al., Chin. Phys. C46, 024101 (2022)

  • [31]

    T. Bayram, C. M. Yeşilkanat, and S. Akkoyun, Phys. Scr.98, 125310 (2023)

  • [32]

    A. Jalili and A.-X. Chen, New J. Phys.26, 103017 (2024)

  • [33]

    D. Wu, C. Bai, H. Sagawaet al., Phys. Rev. C102, 054323 (2020)

  • [34]

    R. Utama, W. C. Chen, and J. Piekarewicz, J. Phys. G: Nucl. Part. Phys.43, 114002 (2016)

  • [35]

    Y. Ma, C. Su, J. Liuet al., Phys. Rev. C101, 014304 (2020)

  • [36]

    X. X. Dong, R. An, J. X. Luet al., Phys. Rev. C105, 014308 (2022)

  • [37]

    X. X. Dong, R. An, J. X. Luet al., Phys. Lett. B.838, 137726 (2023)

  • [38]

    Jian Liu, Kai-Zhong Tan, Lei Wanget al., Nucl Sci Tech.36, 215 (2025)

  • [39]

    T. Li, Y. Luo, and N. Wang, Atomic Data and Nuclear Data Tables140, 101440 (2021)

  • [40]

    I. Angeli and K. Marinova, Atom. Data. Nucl. Data. Tables99, 69 (2013)

  • [41]

    Z. X. Liuet al., At. Data Nucl. Data Tables156, 101635 (2024)

  • [42]

    L. Prokhorenkova, G. Gusevet al., Adv Neural Inf Process Syst.31(2018)

  • [43]

    B. So and E. A. Valdez, arXiv: 2406.16206

  • [44]

    J. H. Friedman, Ann. Stat.29, 1189 (2001)

  • [45]

    T. Chen and C. Guestrin, XgBoost: A scalable tree boosting system, inProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(San Francisco, California, USA, 2016), pp. 785.

  • [46]

    G. Ke, Q. Menget al., Adv. Neural Inf. Process30(2017)

  • [47]

    Y. Freund, R. E. Schapire, ICML.96, 148 (1996)

  • [48]

    T. Akiba, S. Sano,et al.: A next-generation hyperparameter optimization framework, inProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining(Anchorage, AK, USA, 2019)

  • [49]

    J. Bergstra, R. Bardenet, Y. Bengioet al. Algorithms for hyper-parameter optimization, inProceedings of the 25th International Conference on Neural Information Processing Systems(Granada, Spain, 2011)

  • [50]

    S. M. Lundberg and S. I. Lee, A unified approach to interpreting model predictions, inProceedings of the 31st International Conference on Neural Information Processing Systems, 2017, p. 4768

  • [51]

    Shapley, L. S., A value for n-person games.

  • [52]

    S. M. Lundberg, G. G. Erion, and S. I. Lee, arXiv: 1802.03888

  • [53]

    J. W. Tukey,Exploratory data analysis(New York: Springer, 1997)

  • [54]

    Y. Y. Cao, J. Y. Guo, and B. Zhou, Nucl. Sci. Tech.34, 152 (2023)

  • [55]

    Y. Xu, S. Goriely, and A. Jorissen, Astron. Astrophys.549, A106 (2013)

Baidu
map