Discovered Function for Positron Collisions With Alkali-Metal Atoms Using Genetic Programming

El-Bakry, Salah Yaseen; Radi, A.

doi:10.1142/s0129183107009480

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…We notice that our GTB model calculations give an improvement upon the theoretical models. Also, in comparison with our previous work (Genetic Programming model [15,16]) we have found that the GTB model gives best fitting in very short computational time (0.73 sec).…”

Section: Resultssupporting

confidence: 53%

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Total cross section prediction of the collisions of positrons and electrons with alkali atoms using Gradient Tree Boosting

2011

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A relatively new computational technique, namely gradient tree boosting (GTB), is presented for modeling the total cross sections of the scattering of positrons and electrons by alkali atoms in the low and intermediate energy regions. The calculations have been performed in the framework of gradient tree boosting (GTB). The GTB has been running based on the experimental data of the total collisional cross sections to produce the total cross sections for each alkali atom as a function of the incident energy of the projectile as well as the atomic number and the static dipole polarizability of the atom. Moreover our GTB model is used to predict the experimental data for total collisional cross sections that are not used in the training session. The calculated and predicted total collisional cross sections are compared with the experimental data. We find that the GTB technique shows a good match to the experimental data. To our knowledge, this is the first application of the GTB technique to the data of positron and electron collisions with alkali atoms at low and intermediate energies.

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Section: Resultssupporting

confidence: 53%

“…Recently, there are a number of machine learning techniques such as neural networks and genetic programming [13][14][15][16][17][18][19][20][21][22] used to predict and model the total cross sections. The reason for applying machine learning methods (based on statistical learning theory or biological inspired techniques, etc.)…”

Section: Introductionmentioning

confidence: 99%

Total cross section prediction of the collisions of positrons and electrons with alkali atoms using Gradient Tree Boosting

2011

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“…AI techniques are becoming very useful as alternate approaches to conventional ones (Whiteson & Whiteson 2009). Within AI, genetic programming (GP) is a global optimization algorithm and an automatic programming technique that has been applied in physics and astrophysics (Cohen et al 2003, El-Bakry & Radi 2007, Teodorescu & Sherwood 2008, El-dahshan 2009, Schmidt & Lipson 2009, Indranil et al 2013. GP is a powerful tool that can be used to solve complex fitting problems.…”

Section: Optimization Approachmentioning

confidence: 99%

Unified numerical model of collisional depolarization and broadening rates that are due to hydrogen atom collisions

Derouich

Radi

Barklem

2015

A&A

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Context. Accounting for partial or complete frequency redistribution when interpreting solar polarization spectra requires data on various collisional processes. Data for depolarization and polarization transfer are needed, but are often lacking, while data for collisional broadening are usually more readily available. Recently it was concluded that despite underlying similarities in the physics of collisional broadening and depolarization processes, the relations between them cannot be derived purely analytically. Aims. We aim to derive accurate numerical relations between the collisional broadening rates and the collisional depolarization and polarization transfer rates that are due to hydrogen atom collisions. These relations would enable accurate and efficient estimates of collisional data for solar applications. Methods. Using earlier results for broadening and depolarization processes based on general (i.e., not specific to a given atom), semi-classical calculations that employ interaction potentials from perturbation theory, we used genetic programming (GP) to fit the available data and generate analytical functions describing the relations between them. The predicted relations from the GP-based model were compared with the original data to estimate the accuracy of the method. Results. We obtain strongly nonlinear relations between the collisional broadening rates and the depolarization and polarization transfer rates. They are shown to reproduce the original data with an accuracy of about 5%. Our results allow determining the depolarization and polarization transfer rates for hyperfine or fine-structure levels of simple and complex atoms. Conclusions. We show that by using a sophisticated numerical approach and a general collision theory, useful relations with sufficient accuracy for applications are possible.

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“…Recently, several modelling methods based on soft computing systems include the application of artificial intelligence (AI) techniques. These evolution algorithms have a physically powerful existence in this field [15][16][17][18][19]. The behavior of p-p and pb-pb interactions are complicated due to the non-linear relationship between the interaction parameters and the output.…”

Section: Introductionmentioning

confidence: 99%

Entropy per rapidity in Pb-Pb central collisions using Thermal and Artificial neural network(ANN) models at LHC energies

Habashy,

El-Bakry,

Scheinast

et al. 2021

Preprint

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The entropy per rapidity dS/dy produced in central Pb-Pb ultra-relativistic nuclear collisions at LHC energies is calculated using experimentally observed identified particle spectra and source radii estimated from Hanbury Brown-Twiss (HBT) for particles, π, k, p, Λ, Ω, and Σ, and π, k, p, Λ and K 0 s at √ s = 2.76 and 5.02 TeV, respectively. Artificial neural network (ANN) simulation model is used to estimate the entropy per rapidity dS/dy at the considered energies. The simulation results are compared with equivalent experimental data, and good agreement is achieved. A mathematical equation describes experimental data is obtained. Extrapolating the transverse momentum spectra at p T = 0 is required to calculate dS/dy thus we use two different fitting functions, Tsallis distribution and the Hadron Resonance Gas (HRG) model. The success of ANN model to describe the experimental measurements will imply further prediction for the entropy per rapidity in the absence of the experiment.

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Discovered Function for Positron Collisions With Alkali-Metal Atoms Using Genetic Programming

Cited by 11 publications

References 19 publications

Total cross section prediction of the collisions of positrons and electrons with alkali atoms using Gradient Tree Boosting

Total cross section prediction of the collisions of positrons and electrons with alkali atoms using Gradient Tree Boosting

Unified numerical model of collisional depolarization and broadening rates that are due to hydrogen atom collisions

Entropy per rapidity in Pb-Pb central collisions using Thermal and Artificial neural network(ANN) models at LHC energies

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