Principal components of nuclear mass models

Wu, Xin-Hui; Zhao, Pengwei

doi:10.1007/s11433-023-2342-4

Cited by 7 publications

(1 citation statement)

References 69 publications

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“…Recently, machine learning (ML) has been widely used in physics and nuclear physics [28][29][30][31]. Due to the special importance of nuclear mass, many ML approaches have been employed to improve its description, such as the kernel ridge regression (KRR) [32][33][34], the radial basis function (RBF) [35,36], the Bayesian neural network (BNN) [37][38][39], the Gaussian process regression [40,41], the principal component analysis [42], etc. Among these approaches, it is found that the KRR has the advantage that it can avoid the risk of worsening the mass predictions for nuclei at large extrapolation, due to the performance of Gaussian kernel function and ridge regression.…”

Section: Introductionmentioning

confidence: 99%

Studies of different kernel functions in nuclear mass predictions with kernel ridge regression

Xin-rong

2023

Front. Phys.

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The kernel ridge regression (KRR) approach has been successfully applied in nuclear mass predictions. Kernel function plays an important role in the KRR approach. In this work, the performances of different kernel functions in nuclear mass predictions are carefully explored. The performances are illustrated by comparing the accuracies of describing experimentally known nuclei and the extrapolation abilities. It is found that the accuracies of describing experimentally known nuclei in the KRR approaches with most of the adopted kernels can reach the same level around 195 keV, and the performance of the Gaussian kernel is slightly better than other ones in the extrapolation validation for the whole range of the extrapolation distances.

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Section: Introductionmentioning

confidence: 99%

Studies of different kernel functions in nuclear mass predictions with kernel ridge regression

Xin-rong

2023

Front. Phys.

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Machine learning the in-medium correction factor on nucleon–nucleon elastic cross section

Wei,

Li,

Wang

et al. 2024

J. Phys. G: Nucl. Part. Phys.

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The nuclear equation of state cannot be directly measured in the heavy-ion collision experiments; it is usually inferred from the comparison between transport model simulations and experimental measurements. The in-medium correction factor (F , which is deﬁned as the ratio of the cross section in the nuclear medium to that in free space) on the nucleon-nucleon elastic cross section is one of the important inputs of the transport model, and its magnitude is still debated. The advent of Machine Learning (ML) has profoundly inﬂuenced the way scientists study the natural world and has provided a new paradigm that merges traditional investigation with advanced datadriven techniques. The aim of this work is to present a ML-based method to study the in-medium correction factor F . The ultra-relativistic quantum molecular dynamics (UrQMD) transport model is used to simulate 132Sn + 124Sn collisions at beam energy of 0.27 GeV/nucleon with diﬀerent impact parameters. The value of F is randomly selected from 0.4 to 0.8 for the simulation of each event. Several observables simulated by the UrQMD model with diﬀerent F , which are thought to be probably sensitive to the in-medium nucleon-nucleon cross sections, are fed into the LightGBM (Light Gradient Boosting Machine, which is a modern decision tree-based ML algorithm) to establish the mapping between the observables and F . The mean absolute error (MAE), which is the absolute diﬀerence between the true and the predicted F , is about 0.080 and 0.021 by using event-by-event and 40-event summed observables, respectively. It indicates that ML can recognize information about the F factor. Furthermore, according to the results of Shapley Additive exPlanations (SHAP), an interpretability analysis method in ML, features that have the greatest eﬀect on the F are identiﬁed. ML combined with the transport model may open a new venue to study the F factor. In addition, the interpretability analysis of the ML algorithm may also oﬀer valuable insights for subsequent research.

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Nuclear mass predictions with anisotropic kernel ridge regression

Wu,

Pan

2024

Phys. Rev. C

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Principal components of nuclear mass models

Cited by 7 publications

References 69 publications

Studies of different kernel functions in nuclear mass predictions with kernel ridge regression

Studies of different kernel functions in nuclear mass predictions with kernel ridge regression

Machine learning the in-medium correction factor on nucleon–nucleon elastic cross section

Nuclear mass predictions with anisotropic kernel ridge regression

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