Utilization of the random forest method for studying some heavy mesons spectra via machine learning technique

Nahool, T. A.; Anwar, M.; Yahya, G. A.; Yasser, A. M.

doi:10.1142/s0217751x22502190

Cited by 7 publications

(6 citation statements)

References 32 publications

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“…For this, α = 0.6 and β = 0.5 are taken according to condition (54) imposed. In our calculations, we use the parameters, m c = 1.4619 GeV , m b = 4.68 GeV , obtained from the references [12,36]. In Table I, the total error is calculated by averaging the relative errors with regard to the experimental data [64].…”

Section: Discussion Of Resultsmentioning

confidence: 99%

“…We observed that the 1S-states (n = 0) of the mesons (cc, bb, bc) are nearby by the experimental and it is improved compared to the results of [12,17] and exhibit good agreement with the experimental evidence [64]. TABLE I: Fundamental energy level and mass spectrum of mesons (cc, bb, bc) in (GeV ) with (λ = 0.1497 GeV 2 , σ = 0.03 GeV 3 , m cc = 0.73095 GeV ), (m bb = 2.34 GeV , λ = 0.465 GeV 2 , σ = 0.143 GeV 3 ), (m bc = 1.11 GeV , λ = 0.2 GeV 2 , σ = 0.04 GeV 3 ) [12,36,64].…”

Section: Discussion Of Resultsmentioning

confidence: 99%

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Quark-Antiquark Effective Potential in Symplectic Quantum Mechanics

Luz

Petronilo

et al. 2022

Advances in High Energy Physics

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In this paper, we study within the structure of Symplectic Quantum Mechanics a bidimensional nonrelativistic strong interaction system which represent the bound state of heavy quark-antiquark, where we consider a Cornell potential which consists of Coulomb-type plus linear potentials. First, we solve the Schrödinger equation in the phase space with the linear potential. The solution (ground state) is obtained and analyzed by means of the Wigner function related to Airy function for the c c ¯ meson. In the second case, to treat the Schrödinger-like equation in the phase space, a procedure based on the Bohlin transformation is presented and applied to the Cornell potential. In this case, the system is separated into two parts, one analogous to the oscillator and the other we treat using perturbation method. Then, we quantized the Hamiltonian with the aid of stars operators in the phase space representation so that we can determine through the algebraic method the eigenfunctions of the undisturbed Hamiltonian (oscillator solution), and the other part of the Hamiltonian was the perturbation method. The eigenfunctions found (undisturbed plus disturbed) are associated with the Wigner function via Weyl product using the representation theory of Galilei group in the phase space. The Wigner function is analyzed, and the nonclassicality of ground state and first excited state is studied by the nonclassicality indicator or negativity parameter of the Wigner function for this system. In some aspects, we observe that the Wigner function offers an easier way to visualize the nonclassic nature of meson system than the wavefunction does phase space.

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Section: Discussion Of Resultsmentioning

confidence: 99%

Section: Discussion Of Resultsmentioning

confidence: 99%

Quark-Antiquark Effective Potential in Symplectic Quantum Mechanics

Luz

Petronilo

et al. 2022

Advances in High Energy Physics

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“…The random forest algorithm is an integrated learning method, which means that it is composed of many small models to be constructed, and the outputs of the individual small models are combined to form the final output [21]. The random forest algorithm is a typical machine learning algorithm for classification, regression, or other learning tasks.…”

Section: Selection Of Decision Tree Attributesmentioning

confidence: 99%

“…The indicators are: (21) metric takes the squared sum of the distance between the centroid of each cluster and the mean center of the sample set as the separation of the data set, the squared sum of the distance between each point in the cluster and the cluster center as the tightness within the cluster, and the ratio of separation to tightness as the final metric of . The larger the metric indicates, the higher the degree of dispersion among the clusters, the tighter the clusters, and the better the evaluation result.…”

Section: Attribute Information Gain Rate Calculationmentioning

confidence: 99%

Research on the Reform of Film and Television Production Courses and Their Student Achievement Evaluation in Colleges and Universities in Shanxi Province Based on Random Forest Algorithm

Shi,

Govindasamy

2023

Applied Mathematics and Nonlinear Sciences

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This paper classifies the data of the original data set by random forest algorithm, selects the nodes in the attribute space for iteration, and gets the number of decision trees in the random forest. Based on the decision tree, the information gain rate in the sample set of student achievement is calculated, the spatial distance matrix of the sample set is defined, and the centroids of each cluster of the matrix indicators are separated to get the evaluation results of indicators in the student achievement evaluation reform as superior. The indicator weights of student learning achievement are evaluated through five assessment indexes, in which the teacher rating weight is the highest 10. It shows that active use of the Internet is conducive to cultivating and delivering excellent film and television production professionals to society.

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“…In the field of geological science, especially in the direction of metallogenic prediction, domestic and foreign scholars have adopted a variety of machine-learning algorithms [28][29][30]. Among them, the decision tree algorithm (DT), random forest algorithm (RF), support vector machine (SVM), and artificial neural network (ANN) are the most widely used in geosciences [31][32][33][34]. Among them, DT-based algorithms need to estimate fewer parameters and are easy to apply.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Au-Polymetallic Deposits Based on Spatial Multi-Layer Information Fusion by Random Forest Model in the Central Kunlun Area of Xinjiang, China

Zhang,

Ye,

Xie

et al. 2023

Minerals

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In recent years, there has been a growing emphasis on combining intelligent prospecting algorithms, such as random forest, with extensive geological and mineral data for the purpose of quantitatively predicting exploration geochemistry. This approach holds significant importance for enhancing the accuracy of target delineation. The central Kunlun area in Xinjiang possesses highly favorable ore-forming geological conditions, offering excellent prospects for mineral exploration. However, the depletion of shallow deposits coupled with a decade-long gap in geological exploration have presented increasing challenges in the quest to discover substantial metal resources. Consequently, there is now a severe shortage of reserve assets in the region, prompting an urgent need for the implementation of new theories, methods, and technologies in mineral resource investigation and evaluation efforts. The researchers used geological and regional geochemical data to construct a random forest metallogenic discriminant model for predicting the mineralization of gold polymetallic minerals in the central Kunlun area of Xinjiang and delineating the metallogenic target area. Two different sampling methods were compared to quantitatively predict gold polymetallic mineral resources. The results indicate that the selected training samples offer higher prediction accuracy and reliability by fully capturing the complex information of the original data. The random forest model using select training samples has valuable applications in metallogenic prospect prediction and potential division due to its ability to consider the actual exploration cost and identify small areas with high potential and a high proportion of ore. This study significantly improves prediction accuracy, reduces exploration risk, and expands the use of machine learning algorithms in mathematical geology in the central Kunlun area of Xinjiang.

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Utilization of the random forest method for studying some heavy mesons spectra via machine learning technique

Cited by 7 publications

References 32 publications

Quark-Antiquark Effective Potential in Symplectic Quantum Mechanics

Quark-Antiquark Effective Potential in Symplectic Quantum Mechanics

Research on the Reform of Film and Television Production Courses and Their Student Achievement Evaluation in Colleges and Universities in Shanxi Province Based on Random Forest Algorithm

Prediction of Au-Polymetallic Deposits Based on Spatial Multi-Layer Information Fusion by Random Forest Model in the Central Kunlun Area of Xinjiang, China

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