TY - JOUR
T1 - Application of gene expression programming for proton-proton interactions at large hadrons collider
AU - Radi, A.
N1 - Funding Information:
Acknowledgments Authors highly acknowledge and deeply appreciate supports of Egyptian Academy of Scientific Research and Technology (ASRT) and Egyptian Network for High Energy Physics (ENHEP).
PY - 2013/6
Y1 - 2013/6
N2 - This paper describes how to use gene expression programming (GEP) as an evolutionary computational optimization approach. GEP, as a machine learning technique is usually used for modeling physical phenomena by discovering a new function. In case of modeling the p-p interactions at large hadrons collider (LHC) experiments, GEP is used to simulate and predict the number of charged particles multiplicity 〈 n 〉 and total cross-section, σ T, as a function of total center-of-mass energy, s. Considering the discovered function for 〈 n 〉 (s), the general trend of the predicted values shows good agreement at LHC [predicted values are 31.3251, 32.8638 and 35.3520 at s = 8 TeV, s = 10 TeV and s = 14 TeV respectively]. The discovered function, trained on experimental data of particle data group shows a good match as compared with the other models. The predicted values of cross section at s = 8, 10 and 14 TeV are found to be 101.0417, 105.0690 and 111.3407 mb respectively. Moreover, those predicted values are in good agreement with those reported by Block, Cudell and Nakamura.
AB - This paper describes how to use gene expression programming (GEP) as an evolutionary computational optimization approach. GEP, as a machine learning technique is usually used for modeling physical phenomena by discovering a new function. In case of modeling the p-p interactions at large hadrons collider (LHC) experiments, GEP is used to simulate and predict the number of charged particles multiplicity 〈 n 〉 and total cross-section, σ T, as a function of total center-of-mass energy, s. Considering the discovered function for 〈 n 〉 (s), the general trend of the predicted values shows good agreement at LHC [predicted values are 31.3251, 32.8638 and 35.3520 at s = 8 TeV, s = 10 TeV and s = 14 TeV respectively]. The discovered function, trained on experimental data of particle data group shows a good match as compared with the other models. The predicted values of cross section at s = 8, 10 and 14 TeV are found to be 101.0417, 105.0690 and 111.3407 mb respectively. Moreover, those predicted values are in good agreement with those reported by Block, Cudell and Nakamura.
KW - Gene expression programming
KW - Machine learning
KW - Modeling
KW - Multiplicity distribution
KW - Proton-proton interaction
UR - http://www.scopus.com/inward/record.url?scp=84879246316&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84879246316&partnerID=8YFLogxK
U2 - 10.1007/s12648-013-0269-5
DO - 10.1007/s12648-013-0269-5
M3 - Article
AN - SCOPUS:84879246316
SN - 0973-1458
VL - 87
SP - 593
EP - 599
JO - Indian Journal of Physics
JF - Indian Journal of Physics
IS - 6
ER -