Application of genetic programming to high energy physics event selection

Link, J. M.; Yager, P. M.; Anjos, J. C.; Bediaga, I.; Castromonte, C.; Göbel, C.; Machado, A. A.; Magnin, J.; Massafferri, A.; Miranda, J. M. De; Pepe, I. M.; Polycarpo, E.; Reis, A. C. dos; Carrillo, S.; Casimiro, E.; Cuautle, E.; Sánchez-Hernández, A.; Uribe, C.; Vázquez, F.; Agostino, L.; Cinquini, L.; Cumalat, J. P.; O’Reilly, B.; Segoni, I.; Stenson, K.; Butler, J. N.; Cheung, H. W. K.; Chiodini, G.; Gaines, I.; Garbincius, P. H.; Garren, L.; Gottschalk, E.; Kasper, P.; Kreymer, A.; Kutschke, R.; Wang, M.; Benussi, L.; Bertani, M.; Bianco, S.; Fabbri, F.; Pacetti, S.; Zallo, A.; Reyes, M.; Cawlfield, C.; Kim, D.Y.; Rahimi, A. M.; Wiss, J.; Gardner, R.; Kryemadhi, A.; Chung, Y. S.; Kang, J. S.; Ko, B. R.; Kwak, J. W.; Lee, K.B.; Cho, K.; Park, H.; Alimonti, G.; Barberis, S.; Boschini, M.; Cerutti, Agustín Cobos; D’Angelo, P.; DiCorato, M.; Dini, P.; Edera, L.; Erba, S.; Inzani, P.; Leveraro, F.; Malvezzi, S.; Menasce, D.; Mezzadri, M.; Moroni, L.; Pedrini, D.; Pontoglio, C.; Prelz, F.; Rovere, M.; Sala, S.; Davenport, T. F.; Arena, Vincenzo; Boca, G.; Bonomi, G.; Gianini, G.; Liguori, G.; Pegna, D. Lopes; Merlo, M. M.; Pantea, D.; Ratti, S. P.; Riccardi, C.; Vitulo, P.; Hernandez, H.; López, A.; Méndez, H.; Paris, A.; Quinones, J.; Vargas, J. E. Ramirez; Zhang, Y.; Wilson, J. R.; Handler, T.; Mitchell, R. E.; Engh, D.; Hosack, M.; Johns, W.; Luiggi, E.; Moore, J. E.; Nehring, M.; Sheldon, P.; Vaandering, Eric Wayne; Webster, M.; Sheaff, M.

doi:10.1016/j.nima.2005.05.069

Cited by 27 publications

(14 citation statements)

References 11 publications

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“…8,11 As well, GP 7 have succeeded in the field of 33 automatic define function. 2 Oakley used GP to evolve equations to fit the chaotic time series produced by Mackey-Glass equations.…”

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confidence: 99%

Prediction of Nonlinear System in Optics Using Genetic Programming

Radi

2007

Int. J. Mod. Phys. C

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11It is difficult to predict the dynamics of systems which are nonlinear and whose characteristic is unknown. In order to build a model of the system from input and output 13 data without any knowledge about the system, we try automatically to build prediction model by Genetic Programming (GP). 15GP has been used to discover the function that describes nonlinear system to study the effect of wavelength and temperature on the refractive index of the fiber core. The 17 predicted distribution from the GP based model is compared with the experimental data. The discovered function of the GP model has proved to be an excellent match to 19 the experimental data.

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“…8,11 As well, GP 7 have succeeded in the field of 33 automatic define function. 2 Oakley used GP to evolve equations to fit the chaotic time series produced by Mackey-Glass equations.…”

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confidence: 99%

Prediction of Nonlinear System in Optics Using Genetic Programming

Radi

2007

Int. J. Mod. Phys. C

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“…Recently, computational intelligence (CI) methodologies have become one of the most efficient techniques for the analysis of the charged particles in positron-electron annihilation (High energy physics "HEP" particle interactions) [25][26][27][28][29][30][31]. Generally, CI is a broad term covering a wide range of computational methodologies and approaches (such as artificial neural networks (ANNs), Genetic Programming (GP), ……) and most of them are nature-inspired algorithms.…”

Section: Eejp Vol4 No3 2017mentioning

confidence: 99%

“…Generally, CI is a broad term covering a wide range of computational methodologies and approaches (such as artificial neural networks (ANNs), Genetic Programming (GP), ……) and most of them are nature-inspired algorithms. CI approaches have been used in many fields in particles and nuclear physics as in the other fields, such as studies of event selection problems with Genetic programming (J. M. Link;et.al. 2005) [25], re-discover certain laws of physicssuch as Hamiltonian, Lagrangian, and other laws of geometric and momentum conservation (M. Schmidt, H. Lipson, 2009) [26], estimation of the fission barrier heights of the nuclei (A. Serkan, and T. Bayram, 2014) [27], estimations of beta-decay energies through the Nuclidic Chart (S. Akkoyun et al, 2014) [28] and searching for exotic particles in highenergy physics (P. Baldi et al2016 and 2014) [ 30,31].…”

Section: Eejp Vol4 No3 2017mentioning

confidence: 99%

Empirical Equation Using GMDH Methodology for the Charged Particles Multiplicity Distribution in Hadronic Positron-Electron Annihilation

El‐Dahshan

El-Bakry

2017

EEJP

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Multiplicity distributions are the most general characteristics of hadronic multiparticle production processes. The multiplicity distribution of hadronic positron-electron annihilation is investigated using group method data handling (GMDH) technique up to the highest available center of mass energy ( s ) (from 14 GeV to 206 GeV). We have obtained an empirical physical equation for the multiplicity distribution as a function of s and the charged multiplicity (n ch ) i. e.

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“…The application of artificial intelligence (or the machine learning) such as genetic programming (GP) has a strong presence in the high energy physics [18][19][20][21][22]. The effort to understand the interactions of fundamental particles requires complex data analysis for which machine learning (ML) algorithms are vital.…”

Section: Introductionmentioning

confidence: 99%

“…The complex behavior of the p-p interactions due to the nonlinear relationship between the interaction parameters and the output often becomes complicated. In this sense, ML techniques such as artificial neural network [24], genetic algorithm [25] and genetic programming [26] can be used as alternative tool for the simulation of these interactions [18][19][20][21][22][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Application of genetic programming for proton-proton interactions

El‐Dahshan

2011

Open Physics

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Abstract:The aim of the present work is to use one of the machine learning techniques named the genetic programming (GP) to model the p-p interactions through discovering functions. In our study, GP is used to simulate and predict the multiplicity distribution of charged pions (P( )), the average multiplicity ( ) and the total cross section (σ ) at different values of high energies. We have obtained the multiplicity distribution as a function of the center of mass energy ( √ ) and charged particles ( ). Also, both the average multiplicity and the total cross section are obtained as a function of √ . Our discovered functions produced by GP technique show a good match to the experimental data. The performance of the GP models was also tested at non-trained data and was found to be in good agreement with the experimental data.PACS (

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Application of genetic programming to high energy physics event selection

Cited by 27 publications

References 11 publications

Prediction of Nonlinear System in Optics Using Genetic Programming

Prediction of Nonlinear System in Optics Using Genetic Programming

Empirical Equation Using GMDH Methodology for the Charged Particles Multiplicity Distribution in Hadronic Positron-Electron Annihilation

Application of genetic programming for proton-proton interactions

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