الفهرس 
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Abstract
In this work, a theoretical treatment for studying the interaction of Hadrons with each other in the high energies in the framework of two models. First model, the parton two fireball model(PTFM) based on the impact parameter analysis which depend on the overlapping volume in collision . Second model, the artificial intelligence (AI) which contains the model of the neural networks for simulation these interactions and also prediction the collision of experiments that have not been made yet with describing them by mathematical function obtained from this model.
In the first and second chapters we showed the international scientific experiments of the collisions of the positive and negative pions and also the positive and negative kions each with the protons at different energies. Also, we showed the different theoretical models which can be applied on these interactions with a brief explanation of the bases of these models.
The results in the last three chapters in the dissertation are summarized as follows:
1-In the framework of parton two fireball model (PTFM) based on the impact parameter analysis, the changes in the characteristics of the interactions of proton-proton (P-P) were extracted and also (𝜋—-P)at different energies. The multiplicity distribution of neutral pions were calculated and compared with the corresponding experimental data which show a relative correspondence especially in the high energies.
2-The rapidity distribution for the neutral particles (π) was extracted depending on the parton two fireball model and the distributions were characterized by central plateau and dropping at the ends.
The predictions of the PTFM agree with observations in some features of (P-P and 𝜋—-P) interactions and disagree with others , particularely at low energies.
In addition, we suggested the study of one model from artificial intelligence (AI) which is the model of the neural networks (ANN) for studying these characteristics of the previous interactions and the results were as follows:
1-Many neural networks were designed for studying the elastic and total cross-section for (P±-P , π±-P and K±-P) interaction. The results of the obtained mathematical function simulate and predict the above interaction at different energies. The simulation results performed almost exact fitting to the given experimental data.
2-The network also simulated and calculated the multiplicity distribution of neutral pions for (π--p) and also (p-p) at different energies. The trained ANN shows a better fitting with experimental data. The ANN is used to predict the distributions that are not present in the training set and matching them effectively.
3-The rapidity distribution of the neutral pions (π) was simulated in two cases, (π--p) and (p-p) at different energies through mathematical equations based on model of neural networks. The theoretical results were in good agreement with the experimental data upon which training was made, in addition to that, the ANN is used to predict the experimental data at certain energy that are not present in the training set and matching them effectively.
4-A comparison between the two models (PTFM and ANN) was made through the rapidity distribution of the neutral pions (π) in two cases, (π--p) and (p-p) at different energies. The distributions simulated using ANN showed perfect fitting to the experimental data than parton two fireballs model. Then, the ANN technique is able to exactly model for rapidity distribution at different momenta.
This study refers to that patron two fireballs model can be suitable for the interaction of (π--p) and also (p-p) at high energies with some deviation from experimental data but the model of the neural network in all interactions was of high efficiency with the experimental data. Also The ANN is used to predict the distributions (with certain energy) that are not present in the training set and matching them effectively. The results of ANN amply demonstrate the feasibility of such technique in extracting the collision features and prove its effectiveness.