Modeling p′p and recent LHC pp total cross-sections

Amr Radi, Esraa El-Khateeb*

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةArticleمراجعة النظراء

1 اقتباس (Scopus)


New technique is presented for modeling total cross-section of both pp and $\bar{p}p$ collisions from low to ultra high energy regions using an efficient artificial neural network (ANN). We have used the input (center-of-mass energy, √s, and type of particle P) and output (total cross-section σtot) data to build a prediction model by ANN. The neural network has been trained to produce a function that studies the dependence of σtot on √s and P. The trained ANN model shows a good performance in matching the trained distributions, predicts cross-sections that are not presented in the training set. The general trend of the predicted values shows a good agreement with the recent Large Hadron Collider (LHC) measurements, where the total cross-section at √s = 7∼{\rm TeV}$ and 8 TeV are measured to be 98.6 mb and 101.7 mb, respectively. The predicted values of the total cross-section at √s = 10∼{\rm TeV} and 14 TeV are found to be 105.8 mb and 111.7 mb, respectively. Those predictions are in good agreement with Block, Cudell and Nakamura.

اللغة الأصليةEnglish
رقم المقال1450044
دوريةModern Physics Letters A
مستوى الصوت29
رقم الإصدار8
المعرِّفات الرقمية للأشياء
حالة النشرPublished - مارس 14 2014

ASJC Scopus subject areas

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